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• Correlational Research | Guide, Design & Examples

Correlational Research | Guide, Design & Examples

Published on 5 May 2022 by Pritha Bhandari . Revised on 5 December 2022.

A correlational research design investigates relationships between variables without the researcher controlling or manipulating any of them.

A correlation reflects the strength and/or direction of the relationship between two (or more) variables. The direction of a correlation can be either positive or negative.

Table of contents

Correlational vs experimental research, when to use correlational research, how to collect correlational data, how to analyse correlational data, correlation and causation, frequently asked questions about correlational research.

Correlational and experimental research both use quantitative methods to investigate relationships between variables. But there are important differences in how data is collected and the types of conclusions you can draw.

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Correlational research is ideal for gathering data quickly from natural settings. That helps you generalise your findings to real-life situations in an externally valid way.

There are a few situations where correlational research is an appropriate choice.

To investigate non-causal relationships

You want to find out if there is an association between two variables, but you don’t expect to find a causal relationship between them.

Correlational research can provide insights into complex real-world relationships, helping researchers develop theories and make predictions.

To explore causal relationships between variables

You think there is a causal relationship between two variables, but it is impractical, unethical, or too costly to conduct experimental research that manipulates one of the variables.

Correlational research can provide initial indications or additional support for theories about causal relationships.

To test new measurement tools

You have developed a new instrument for measuring your variable, and you need to test its reliability or validity .

Correlational research can be used to assess whether a tool consistently or accurately captures the concept it aims to measure.

There are many different methods you can use in correlational research. In the social and behavioural sciences, the most common data collection methods for this type of research include surveys, observations, and secondary data.

It’s important to carefully choose and plan your methods to ensure the reliability and validity of your results. You should carefully select a representative sample so that your data reflects the population you’re interested in without bias .

In survey research , you can use questionnaires to measure your variables of interest. You can conduct surveys online, by post, by phone, or in person.

Surveys are a quick, flexible way to collect standardised data from many participants, but it’s important to ensure that your questions are worded in an unbiased way and capture relevant insights.

Naturalistic observation

Naturalistic observation is a type of field research where you gather data about a behaviour or phenomenon in its natural environment.

This method often involves recording, counting, describing, and categorising actions and events. Naturalistic observation can include both qualitative and quantitative elements, but to assess correlation, you collect data that can be analysed quantitatively (e.g., frequencies, durations, scales, and amounts).

Naturalistic observation lets you easily generalise your results to real-world contexts, and you can study experiences that aren’t replicable in lab settings. But data analysis can be time-consuming and unpredictable, and researcher bias may skew the interpretations.

Secondary data

Instead of collecting original data, you can also use data that has already been collected for a different purpose, such as official records, polls, or previous studies.

Using secondary data is inexpensive and fast, because data collection is complete. However, the data may be unreliable, incomplete, or not entirely relevant, and you have no control over the reliability or validity of the data collection procedures.

After collecting data, you can statistically analyse the relationship between variables using correlation or regression analyses, or both. You can also visualise the relationships between variables with a scatterplot.

Different types of correlation coefficients and regression analyses are appropriate for your data based on their levels of measurement and distributions .

Correlation analysis

Using a correlation analysis, you can summarise the relationship between variables into a correlation coefficient : a single number that describes the strength and direction of the relationship between variables. With this number, you’ll quantify the degree of the relationship between variables.

The Pearson product-moment correlation coefficient, also known as Pearson’s r , is commonly used for assessing a linear relationship between two quantitative variables.

Correlation coefficients are usually found for two variables at a time, but you can use a multiple correlation coefficient for three or more variables.

Regression analysis

With a regression analysis , you can predict how much a change in one variable will be associated with a change in the other variable. The result is a regression equation that describes the line on a graph of your variables.

You can use this equation to predict the value of one variable based on the given value(s) of the other variable(s). It’s best to perform a regression analysis after testing for a correlation between your variables.

It’s important to remember that correlation does not imply causation . Just because you find a correlation between two things doesn’t mean you can conclude one of them causes the other, for a few reasons.

Directionality problem

If two variables are correlated, it could be because one of them is a cause and the other is an effect. But the correlational research design doesn’t allow you to infer which is which. To err on the side of caution, researchers don’t conclude causality from correlational studies.

Third variable problem

A confounding variable is a third variable that influences other variables to make them seem causally related even though they are not. Instead, there are separate causal links between the confounder and each variable.

In correlational research, there’s limited or no researcher control over extraneous variables . Even if you statistically control for some potential confounders, there may still be other hidden variables that disguise the relationship between your study variables.

Although a correlational study can’t demonstrate causation on its own, it can help you develop a causal hypothesis that’s tested in controlled experiments.

A correlation reflects the strength and/or direction of the association between two or more variables.

• A positive correlation means that both variables change in the same direction.
• A negative correlation means that the variables change in opposite directions.
• A zero correlation means there’s no relationship between the variables.

A correlational research design investigates relationships between two variables (or more) without the researcher controlling or manipulating any of them. It’s a non-experimental type of quantitative research .

Controlled experiments establish causality, whereas correlational studies only show associations between variables.

• In an experimental design , you manipulate an independent variable and measure its effect on a dependent variable. Other variables are controlled so they can’t impact the results.
• In a correlational design , you measure variables without manipulating any of them. You can test whether your variables change together, but you can’t be sure that one variable caused a change in another.

In general, correlational research is high in external validity while experimental research is high in internal validity .

A correlation is usually tested for two variables at a time, but you can test correlations between three or more variables.

A correlation coefficient is a single number that describes the strength and direction of the relationship between your variables.

Different types of correlation coefficients might be appropriate for your data based on their levels of measurement and distributions . The Pearson product-moment correlation coefficient (Pearson’s r ) is commonly used to assess a linear relationship between two quantitative variables.

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Home » Correlational Research – Methods, Types and Examples

Correlational Research – Methods, Types and Examples

Table of Contents

Correlational Research

Correlational Research is a type of research that examines the statistical relationship between two or more variables without manipulating them. It is a non-experimental research design that seeks to establish the degree of association or correlation between two or more variables.

Types of Correlational Research

There are three types of correlational research:

Positive Correlation

A positive correlation occurs when two variables increase or decrease together. This means that as one variable increases, the other variable also tends to increase. Similarly, as one variable decreases, the other variable also tends to decrease. For example, there is a positive correlation between the amount of time spent studying and academic performance. The more time a student spends studying, the higher their academic performance is likely to be. Similarly, there is a positive correlation between a person’s age and their income level. As a person gets older, they tend to earn more money.

Negative Correlation

A negative correlation occurs when one variable increases while the other decreases. This means that as one variable increases, the other variable tends to decrease. Similarly, as one variable decreases, the other variable tends to increase. For example, there is a negative correlation between the number of hours spent watching TV and physical activity level. The more time a person spends watching TV, the less physically active they are likely to be. Similarly, there is a negative correlation between the amount of stress a person experiences and their overall happiness. As stress levels increase, happiness levels tend to decrease.

Zero Correlation

A zero correlation occurs when there is no relationship between two variables. This means that the variables are unrelated and do not affect each other. For example, there is zero correlation between a person’s shoe size and their IQ score. The size of a person’s feet has no relationship to their level of intelligence. Similarly, there is zero correlation between a person’s height and their favorite color. The two variables are unrelated to each other.

Correlational Research Methods

Correlational research can be conducted using different methods, including:

Surveys are a common method used in correlational research. Researchers collect data by asking participants to complete questionnaires or surveys that measure different variables of interest. Surveys are useful for exploring the relationships between variables such as personality traits, attitudes, and behaviors.

Observational Studies

Observational studies involve observing and recording the behavior of participants in natural settings. Researchers can use observational studies to examine the relationships between variables such as social interactions, group dynamics, and communication patterns.

Archival Data

Archival data involves using existing data sources such as historical records, census data, or medical records to explore the relationships between variables. Archival data is useful for investigating the relationships between variables that cannot be manipulated or controlled.

Experimental Design

While correlational research does not involve manipulating variables, researchers can use experimental design to establish cause-and-effect relationships between variables. Experimental design involves manipulating one variable while holding other variables constant to determine the effect on the dependent variable.

Meta-Analysis

Meta-analysis involves combining and analyzing the results of multiple studies to explore the relationships between variables across different contexts and populations. Meta-analysis is useful for identifying patterns and inconsistencies in the literature and can provide insights into the strength and direction of relationships between variables.

Data Analysis Methods

Correlational research data analysis methods depend on the type of data collected and the research questions being investigated. Here are some common data analysis methods used in correlational research:

Correlation Coefficient

A correlation coefficient is a statistical measure that quantifies the strength and direction of the relationship between two variables. The correlation coefficient ranges from -1 to +1, with -1 indicating a perfect negative correlation, +1 indicating a perfect positive correlation, and 0 indicating no correlation. Researchers use correlation coefficients to determine the degree to which two variables are related.

Scatterplots

A scatterplot is a graphical representation of the relationship between two variables. Each data point on the plot represents a single observation. The x-axis represents one variable, and the y-axis represents the other variable. The pattern of data points on the plot can provide insights into the strength and direction of the relationship between the two variables.

Regression Analysis

Regression analysis is a statistical method used to model the relationship between two or more variables. Researchers use regression analysis to predict the value of one variable based on the value of another variable. Regression analysis can help identify the strength and direction of the relationship between variables, as well as the degree to which one variable can be used to predict the other.

Factor Analysis

Factor analysis is a statistical method used to identify patterns among variables. Researchers use factor analysis to group variables into factors that are related to each other. Factor analysis can help identify underlying factors that influence the relationship between two variables.

Path Analysis

Path analysis is a statistical method used to model the relationship between multiple variables. Researchers use path analysis to test causal models and identify direct and indirect effects between variables.

Applications of Correlational Research

Correlational research has many practical applications in various fields, including:

• Psychology : Correlational research is commonly used in psychology to explore the relationships between variables such as personality traits, behaviors, and mental health outcomes. For example, researchers may use correlational research to examine the relationship between anxiety and depression, or the relationship between self-esteem and academic achievement.
• Education : Correlational research is useful in educational research to explore the relationships between variables such as teaching methods, student motivation, and academic performance. For example, researchers may use correlational research to examine the relationship between student engagement and academic success, or the relationship between teacher feedback and student learning outcomes.
• Business : Correlational research can be used in business to explore the relationships between variables such as consumer behavior, marketing strategies, and sales outcomes. For example, marketers may use correlational research to examine the relationship between advertising spending and sales revenue, or the relationship between customer satisfaction and brand loyalty.
• Medicine : Correlational research is useful in medical research to explore the relationships between variables such as risk factors, disease outcomes, and treatment effectiveness. For example, researchers may use correlational research to examine the relationship between smoking and lung cancer, or the relationship between exercise and heart health.
• Social Science : Correlational research is commonly used in social science research to explore the relationships between variables such as socioeconomic status, cultural factors, and social behavior. For example, researchers may use correlational research to examine the relationship between income and voting behavior, or the relationship between cultural values and attitudes towards immigration.

Examples of Correlational Research

• Psychology : Researchers might be interested in exploring the relationship between two variables, such as parental attachment and anxiety levels in young adults. The study could involve measuring levels of attachment and anxiety using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying potential risk factors for anxiety in young adults, and in developing interventions that could help improve attachment and reduce anxiety.
• Education : In a correlational study in education, researchers might investigate the relationship between two variables, such as teacher engagement and student motivation in a classroom setting. The study could involve measuring levels of teacher engagement and student motivation using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying strategies that teachers could use to improve student motivation and engagement in the classroom.
• Business : Researchers might explore the relationship between two variables, such as employee satisfaction and productivity levels in a company. The study could involve measuring levels of employee satisfaction and productivity using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying factors that could help increase productivity and improve job satisfaction among employees.
• Medicine : Researchers might examine the relationship between two variables, such as smoking and the risk of developing lung cancer. The study could involve collecting data on smoking habits and lung cancer diagnoses, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in identifying risk factors for lung cancer and in developing interventions that could help reduce smoking rates.
• Sociology : Researchers might investigate the relationship between two variables, such as income levels and political attitudes. The study could involve measuring income levels and political attitudes using established scales or questionnaires, and then analyzing the data to determine if there is a correlation between the two variables. This information could be useful in understanding how socioeconomic factors can influence political beliefs and attitudes.

How to Conduct Correlational Research

Here are the general steps to conduct correlational research:

• Identify the Research Question : Start by identifying the research question that you want to explore. It should involve two or more variables that you want to investigate for a correlation.
• Choose the research method: Decide on the research method that will be most appropriate for your research question. The most common methods for correlational research are surveys, archival research, and naturalistic observation.
• Choose the Sample: Select the participants or data sources that you will use in your study. Your sample should be representative of the population you want to generalize the results to.
• Measure the variables: Choose the measures that will be used to assess the variables of interest. Ensure that the measures are reliable and valid.
• Collect the Data: Collect the data from your sample using the chosen research method. Be sure to maintain ethical standards and obtain informed consent from your participants.
• Analyze the data: Use statistical software to analyze the data and compute the correlation coefficient. This will help you determine the strength and direction of the correlation between the variables.
• Interpret the results: Interpret the results and draw conclusions based on the findings. Consider any limitations or alternative explanations for the results.
• Report the findings: Report the findings of your study in a research report or manuscript. Be sure to include the research question, methods, results, and conclusions.

Purpose of Correlational Research

The purpose of correlational research is to examine the relationship between two or more variables. Correlational research allows researchers to identify whether there is a relationship between variables, and if so, the strength and direction of that relationship. This information can be useful for predicting and explaining behavior, and for identifying potential risk factors or areas for intervention.

Correlational research can be used in a variety of fields, including psychology, education, medicine, business, and sociology. For example, in psychology, correlational research can be used to explore the relationship between personality traits and behavior, or between early life experiences and later mental health outcomes. In education, correlational research can be used to examine the relationship between teaching practices and student achievement. In medicine, correlational research can be used to investigate the relationship between lifestyle factors and disease outcomes.

Overall, the purpose of correlational research is to provide insight into the relationship between variables, which can be used to inform further research, interventions, or policy decisions.

When to use Correlational Research

Here are some situations when correlational research can be particularly useful:

• When experimental research is not possible or ethical: In some situations, it may not be possible or ethical to manipulate variables in an experimental design. In these cases, correlational research can be used to explore the relationship between variables without manipulating them.
• When exploring new areas of research: Correlational research can be useful when exploring new areas of research or when researchers are unsure of the direction of the relationship between variables. Correlational research can help identify potential areas for further investigation.
• When testing theories: Correlational research can be useful for testing theories about the relationship between variables. Researchers can use correlational research to examine the relationship between variables predicted by a theory, and to determine whether the theory is supported by the data.
• When making predictions: Correlational research can be used to make predictions about future behavior or outcomes. For example, if there is a strong positive correlation between education level and income, one could predict that individuals with higher levels of education will have higher incomes.
• When identifying risk factors: Correlational research can be useful for identifying potential risk factors for negative outcomes. For example, a study might find a positive correlation between drug use and depression, indicating that drug use could be a risk factor for depression.

Characteristics of Correlational Research

Here are some common characteristics of correlational research:

• Examines the relationship between two or more variables: Correlational research is designed to examine the relationship between two or more variables. It seeks to determine if there is a relationship between the variables, and if so, the strength and direction of that relationship.
• Non-experimental design: Correlational research is typically non-experimental in design, meaning that the researcher does not manipulate any variables. Instead, the researcher observes and measures the variables as they naturally occur.
• Cannot establish causation : Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. Instead, it only provides information about the relationship between the variables.
• Uses statistical analysis: Correlational research relies on statistical analysis to determine the strength and direction of the relationship between variables. This may include calculating correlation coefficients, regression analysis, or other statistical tests.
• Observes real-world phenomena : Correlational research is often used to observe real-world phenomena, such as the relationship between education and income or the relationship between stress and physical health.
• Can be conducted in a variety of fields : Correlational research can be conducted in a variety of fields, including psychology, sociology, education, and medicine.
• Can be conducted using different methods: Correlational research can be conducted using a variety of methods, including surveys, observational studies, and archival studies.

Advantages of Correlational Research

There are several advantages of using correlational research in a study:

• Allows for the exploration of relationships: Correlational research allows researchers to explore the relationships between variables in a natural setting without manipulating any variables. This can help identify possible relationships between variables that may not have been previously considered.
• Useful for predicting behavior: Correlational research can be useful for predicting future behavior. If a strong correlation is found between two variables, researchers can use this information to predict how changes in one variable may affect the other.
• Can be conducted in real-world settings: Correlational research can be conducted in real-world settings, which allows for the collection of data that is representative of real-world phenomena.
• Can be less expensive and time-consuming than experimental research: Correlational research is often less expensive and time-consuming than experimental research, as it does not involve manipulating variables or creating controlled conditions.
• Useful in identifying risk factors: Correlational research can be used to identify potential risk factors for negative outcomes. By identifying variables that are correlated with negative outcomes, researchers can develop interventions or policies to reduce the risk of negative outcomes.
• Useful in exploring new areas of research: Correlational research can be useful in exploring new areas of research, particularly when researchers are unsure of the direction of the relationship between variables. By conducting correlational research, researchers can identify potential areas for further investigation.

Limitation of Correlational Research

Correlational research also has several limitations that should be taken into account:

• Cannot establish causation: Correlational research cannot establish causation, meaning that it cannot determine whether one variable causes changes in another variable. This is because it is not possible to control all possible confounding variables that could affect the relationship between the variables being studied.
• Directionality problem: The directionality problem refers to the difficulty of determining which variable is influencing the other. For example, a correlation may exist between happiness and social support, but it is not clear whether social support causes happiness, or whether happy people are more likely to have social support.
• Third variable problem: The third variable problem refers to the possibility that a third variable, not included in the study, is responsible for the observed relationship between the two variables being studied.
• Limited generalizability: Correlational research is often limited in terms of its generalizability to other populations or settings. This is because the sample studied may not be representative of the larger population, or because the variables studied may behave differently in different contexts.
• Relies on self-reported data: Correlational research often relies on self-reported data, which can be subject to social desirability bias or other forms of response bias.
• Limited in explaining complex behaviors: Correlational research is limited in explaining complex behaviors that are influenced by multiple factors, such as personality traits, situational factors, and social context.

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7.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of nonexperimental research.

What Is Correlational Research?

Correlational research is a type of nonexperimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are essentially two reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms independent variable and dependent variable do not apply to this kind of research.

The other reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher cannot manipulate the independent variable because it is impossible, impractical, or unethical. For example, Allen Kanner and his colleagues thought that the number of “daily hassles” (e.g., rude salespeople, heavy traffic) that people experience affects the number of physical and psychological symptoms they have (Kanner, Coyne, Schaefer, & Lazarus, 1981). But because they could not manipulate the number of daily hassles their participants experienced, they had to settle for measuring the number of daily hassles—along with the number of symptoms—using self-report questionnaires. Although the strong positive relationship they found between these two variables is consistent with their idea that hassles cause symptoms, it is also consistent with the idea that symptoms cause hassles or that some third variable (e.g., neuroticism) causes both.

A common misconception among beginning researchers is that correlational research must involve two quantitative variables, such as scores on two extraversion tests or the number of hassles and number of symptoms people have experienced. However, the defining feature of correlational research is that the two variables are measured—neither one is manipulated—and this is true regardless of whether the variables are quantitative or categorical. Imagine, for example, that a researcher administers the Rosenberg Self-Esteem Scale to 50 American college students and 50 Japanese college students. Although this “feels” like a between-subjects experiment, it is a correlational study because the researcher did not manipulate the students’ nationalities. The same is true of the study by Cacioppo and Petty comparing college faculty and factory workers in terms of their need for cognition. It is a correlational study because the researchers did not manipulate the participants’ occupations.

Figure 7.2 “Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists” shows data from a hypothetical study on the relationship between whether people make a daily list of things to do (a “to-do list”) and stress. Notice that it is unclear whether this is an experiment or a correlational study because it is unclear whether the independent variable was manipulated. If the researcher randomly assigned some participants to make daily to-do lists and others not to, then it is an experiment. If the researcher simply asked participants whether they made daily to-do lists, then it is a correlational study. The distinction is important because if the study was an experiment, then it could be concluded that making the daily to-do lists reduced participants’ stress. But if it was a correlational study, it could only be concluded that these variables are statistically related. Perhaps being stressed has a negative effect on people’s ability to plan ahead (the directionality problem). Or perhaps people who are more conscientious are more likely to make to-do lists and less likely to be stressed (the third-variable problem). The crucial point is that what defines a study as experimental or correlational is not the variables being studied, nor whether the variables are quantitative or categorical, nor the type of graph or statistics used to analyze the data. It is how the study is conducted.

Figure 7.2 Results of a Hypothetical Study on Whether People Who Make Daily To-Do Lists Experience Less Stress Than People Who Do Not Make Such Lists

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. However, because some approaches to data collection are strongly associated with correlational research, it makes sense to discuss them here. The two we will focus on are naturalistic observation and archival data. A third, survey research, is discussed in its own chapter.

Naturalistic Observation

Naturalistic observation is an approach to data collection that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). It could involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are often not aware that they are being studied. Ethically, this is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated.

Researchers Robert Levine and Ara Norenzayan used naturalistic observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999). One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in the United States and Japan covered 60 feet in about 12 seconds on average, while people in Brazil and Romania took close to 17 seconds.

Because naturalistic observation takes place in the complex and even chaotic “real world,” there are two closely related issues that researchers must deal with before collecting data. The first is sampling. When, where, and under what conditions will the observations be made, and who exactly will be observed? Levine and Norenzayan described their sampling process as follows:

Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities. (p. 186)

Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.

The second issue is measurement. What specific behaviors will be observed? In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance. Often, however, the behaviors of interest are not so obvious or objective. For example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979). But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

Naturalistic observation has revealed that bowlers tend to smile when they turn away from the pins and toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

sieneke toering – bowling big lebowski style – CC BY-NC-ND 2.0.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This is the issue of interrater reliability. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

Archival Data

Another approach to correlational research is the use of archival data , which are data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005). In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988). In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them, were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as college students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as college students, the healthier they were as older men. Pearson’s r was +.25.

This is an example of content analysis —a family of systematic approaches to measurement using complex archival data. Just as naturalistic observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlational research is not defined by where or how the data are collected. However, some approaches to data collection are strongly associated with correlational research. These include naturalistic observation (in which researchers observe people’s behavior in the context in which it normally occurs) and the use of archival data that were already collected for some other purpose.

Discussion: For each of the following, decide whether it is most likely that the study described is experimental or correlational and explain why.

• An educational researcher compares the academic performance of students from the “rich” side of town with that of students from the “poor” side of town.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

Kanner, A. D., Coyne, J. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes of stress measurement: Daily hassles and uplifts versus major life events. Journal of Behavioral Medicine, 4 , 1–39.

Kraut, R. E., & Johnston, R. E. (1979). Social and emotional messages of smiling: An ethological approach. Journal of Personality and Social Psychology, 37 , 1539–1553.

Levine, R. V., & Norenzayan, A. (1999). The pace of life in 31 countries. Journal of Cross-Cultural Psychology, 30 , 178–205.

Pelham, B. W., Carvallo, M., & Jones, J. T. (2005). Implicit egotism. Current Directions in Psychological Science, 14 , 106–110.

Peterson, C., Seligman, M. E. P., & Vaillant, G. E. (1988). Pessimistic explanatory style is a risk factor for physical illness: A thirty-five year longitudinal study. Journal of Personality and Social Psychology, 55 , 23–27.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Correlational Research: What it is with Examples

Our minds can do some brilliant things. For example, it can memorize the jingle of a pizza truck. The louder the jingle, the closer the pizza truck is to us. Who taught us that? Nobody! We relied on our understanding and came to a conclusion. We don’t stop there, do we? If there are multiple pizza trucks in the area and each one has a different jingle, we would memorize it all and relate the jingle to its pizza truck.

This is what correlational research precisely is, establishing a relationship between two variables, “jingle” and “distance of the truck” in this particular example. The correlational study looks for variables that seem to interact with each other. When you see one variable changing, you have a fair idea of how the other variable will change.

What is Correlational research?

Correlational research is a type of non-experimental research method in which a researcher measures two variables and understands and assesses the statistical relationship between them with no influence from any extraneous variable. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities.

Correlational Research Example

The correlation coefficient shows the correlation between two variables (A correlation coefficient is a statistical measure that calculates the strength of the relationship between two variables), a value measured between -1 and +1. When the correlation coefficient is close to +1, there is a positive correlation between the two variables. If the value is relative to -1, there is a negative correlation between the two variables. When the value is close to zero, then there is no relationship between the two variables.

Let us take an example to understand correlational research.

Consider hypothetically, a researcher is studying a correlation between cancer and marriage. In this study, there are two variables: disease and marriage. Let us say marriage has a negative association with cancer. This means that married people are less likely to develop cancer.

However, this doesn’t necessarily mean that marriage directly avoids cancer. In correlational research, it is not possible to establish the fact, what causes what. It is a misconception that a correlational study involves two quantitative variables. However, the reality is two variables are measured, but neither is changed. This is true independent of whether the variables are quantitative or categorical.

Types of correlational research

Mainly three types of correlational research have been identified:

1. Positive correlation: A positive relationship between two variables is when an increase in one variable leads to a rise in the other variable. A decrease in one variable will see a reduction in the other variable. For example, the amount of money a person has might positively correlate with the number of cars the person owns.

2. Negative correlation: A negative correlation is quite literally the opposite of a positive relationship. If there is an increase in one variable, the second variable will show a decrease, and vice versa.

For example, being educated might negatively correlate with the crime rate when an increase in one variable leads to a decrease in another and vice versa. If a country’s education level is improved, it can lower crime rates. Please note that this doesn’t mean that lack of education leads to crimes. It only means that a lack of education and crime is believed to have a common reason – poverty.

3. No correlation: There is no correlation between the two variables in this third type . A change in one variable may not necessarily see a difference in the other variable. For example, being a millionaire and happiness are not correlated. An increase in money doesn’t lead to happiness.

Characteristics of correlational research

Correlational research has three main characteristics. They are: 

• Non-experimental : The correlational study is non-experimental. It means that researchers need not manipulate variables with a scientific methodology to either agree or disagree with a hypothesis. The researcher only measures and observes the relationship between the variables without altering them or subjecting them to external conditioning.
• Backward-looking : Correlational research only looks back at historical data and observes events in the past. Researchers use it to measure and spot historical patterns between two variables. A correlational study may show a positive relationship between two variables, but this can change in the future.
• Dynamic : The patterns between two variables from correlational research are never constant and are always changing. Two variables having negative correlation research in the past can have a positive correlation relationship in the future due to various factors.

Data collection

The distinctive feature of correlational research is that the researcher can’t manipulate either of the variables involved. It doesn’t matter how or where the variables are measured. A researcher could observe participants in a closed environment or a public setting.

Researchers use two data collection methods to collect information in correlational research.

01. Naturalistic observation

Naturalistic observation is a way of data collection in which people’s behavioral targeting is observed in their natural environment, in which they typically exist. This method is a type of field research. It could mean a researcher might be observing people in a grocery store, at the cinema, playground, or in similar places.

Researchers who are usually involved in this type of data collection make observations as unobtrusively as possible so that the participants involved in the study are not aware that they are being observed else they might deviate from being their natural self.

Ethically this method is acceptable if the participants remain anonymous, and if the study is conducted in a public setting, a place where people would not normally expect complete privacy. As mentioned previously, taking an example of the grocery store where people can be observed while collecting an item from the aisle and putting in the shopping bags. This is ethically acceptable, which is why most researchers choose public settings for recording their observations. This data collection method could be both qualitative and quantitative . If you need to know more about qualitative data, you can explore our newly published blog, “ Examples of Qualitative Data in Education .”

02. Archival data

Another approach to correlational data is the use of archival data. Archival information is the data that has been previously collected by doing similar kinds of research . Archival data is usually made available through primary research .

In contrast to naturalistic observation, the information collected through archived data can be pretty straightforward. For example, counting the number of people named Richard in the various states of America based on social security records is relatively short.

Use the correlational research method to conduct a correlational study and measure the statistical relationship between two variables. Uncover the insights that matter the most. Use QuestionPro’s research platform to uncover complex insights that can propel your business to the forefront of your industry.

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Correlational Research

• Living reference work entry
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• Li Shouxin 2  

Correlational research is a psychological research method that examines the relationship between two or more variables using statistical techniques based on the research objectives. It typically involves collecting data on research variables using methods such as tests, surveys, interviews, and natural observations, without intervening or manipulating the variables.

Through correlational research, the main aim is to determine whether there is a positive or negative correlation between the research variables. If two variables are positively correlated, it indicates that they exhibit a similar changing trend. On the other hand, if two variables are negatively correlated, it suggests that they have an opposite changing trend (Fig. 1 ).

Graphs for correlations

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Kantowitz BH, Roediger HL, Elmes DG (2015) Experimental psychology, 10th edn. Cengage Learning, Boston

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Zhang X-M, Shu H (2014) Experimental psychology. Beijing Normal University Publishing Group, Beijing

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Shouxin, L. (2024). Correlational Research. In: The ECPH Encyclopedia of Psychology. Springer, Singapore. https://doi.org/10.1007/978-981-99-6000-2_419-1

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6.2 Correlational Research

Learning objectives.

• Define correlational research and give several examples.
• Explain why a researcher might choose to conduct correlational research rather than experimental research or another type of non-experimental research.
• Interpret the strength and direction of different correlation coefficients.
• Explain why correlation does not imply causation.

What Is Correlational Research?

Correlational research is a type of non-experimental research in which the researcher measures two variables and assesses the statistical relationship (i.e., the correlation) between them with little or no effort to control extraneous variables. There are many reasons that researchers interested in statistical relationships between variables would choose to conduct a correlational study rather than an experiment. The first is that they do not believe that the statistical relationship is a causal one or are not interested in causal relationships. Recall two goals of science are to describe and to predict and the correlational research strategy allows researchers to achieve both of these goals. Specifically, this strategy can be used to describe the strength and direction of the relationship between two variables and if there is a relationship between the variables then the researchers can use scores on one variable to predict scores on the other (using a statistical technique called regression).

Another reason that researchers would choose to use a correlational study rather than an experiment is that the statistical relationship of interest is thought to be causal, but the researcher  cannot  manipulate the independent variable because it is impossible, impractical, or unethical. For example, while I might be interested in the relationship between the frequency people use cannabis and their memory abilities I cannot ethically manipulate the frequency that people use cannabis. As such, I must rely on the correlational research strategy; I must simply measure the frequency that people use cannabis and measure their memory abilities using a standardized test of memory and then determine whether the frequency people use cannabis use is statistically related to memory test performance. 

Correlation is also used to establish the reliability and validity of measurements. For example, a researcher might evaluate the validity of a brief extraversion test by administering it to a large group of participants along with a longer extraversion test that has already been shown to be valid. This researcher might then check to see whether participants’ scores on the brief test are strongly correlated with their scores on the longer one. Neither test score is thought to cause the other, so there is no independent variable to manipulate. In fact, the terms  independent variable  and dependent variabl e  do not apply to this kind of research.

Another strength of correlational research is that it is often higher in external validity than experimental research. Recall there is typically a trade-off between internal validity and external validity. As greater controls are added to experiments, internal validity is increased but often at the expense of external validity. In contrast, correlational studies typically have low internal validity because nothing is manipulated or control but they often have high external validity. Since nothing is manipulated or controlled by the experimenter the results are more likely to reflect relationships that exist in the real world.

Finally, extending upon this trade-off between internal and external validity, correlational research can help to provide converging evidence for a theory. If a theory is supported by a true experiment that is high in internal validity as well as by a correlational study that is high in external validity then the researchers can have more confidence in the validity of their theory. As a concrete example, correlational studies establishing that there is a relationship between watching violent television and aggressive behavior have been complemented by experimental studies confirming that the relationship is a causal one (Bushman & Huesmann, 2001) [1] .  These converging results provide strong evidence that there is a real relationship (indeed a causal relationship) between watching violent television and aggressive behavior.

Data Collection in Correlational Research

Again, the defining feature of correlational research is that neither variable is manipulated. It does not matter how or where the variables are measured. A researcher could have participants come to a laboratory to complete a computerized backward digit span task and a computerized risky decision-making task and then assess the relationship between participants’ scores on the two tasks. Or a researcher could go to a shopping mall to ask people about their attitudes toward the environment and their shopping habits and then assess the relationship between these two variables. Both of these studies would be correlational because no independent variable is manipulated. 

Correlations Between Quantitative Variables

Correlations between quantitative variables are often presented using scatterplots . Figure 6.3 shows some hypothetical data on the relationship between the amount of stress people are under and the number of physical symptoms they have. Each point in the scatterplot represents one person’s score on both variables. For example, the circled point in Figure 6.3 represents a person whose stress score was 10 and who had three physical symptoms. Taking all the points into account, one can see that people under more stress tend to have more physical symptoms. This is a good example of a positive relationship , in which higher scores on one variable tend to be associated with higher scores on the other. A  negative relationship  is one in which higher scores on one variable tend to be associated with lower scores on the other. There is a negative relationship between stress and immune system functioning, for example, because higher stress is associated with lower immune system functioning.

Figure 6.3 Scatterplot Showing a Hypothetical Positive Relationship Between Stress and Number of Physical Symptoms. The circled point represents a person whose stress score was 10 and who had three physical symptoms. Pearson’s r for these data is +.51.

The strength of a correlation between quantitative variables is typically measured using a statistic called  Pearson’s Correlation Coefficient (or Pearson’s  r ) . As Figure 6.4 shows, Pearson’s r ranges from −1.00 (the strongest possible negative relationship) to +1.00 (the strongest possible positive relationship). A value of 0 means there is no relationship between the two variables. When Pearson’s  r  is 0, the points on a scatterplot form a shapeless “cloud.” As its value moves toward −1.00 or +1.00, the points come closer and closer to falling on a single straight line. Correlation coefficients near ±.10 are considered small, values near ± .30 are considered medium, and values near ±.50 are considered large. Notice that the sign of Pearson’s  r  is unrelated to its strength. Pearson’s  r  values of +.30 and −.30, for example, are equally strong; it is just that one represents a moderate positive relationship and the other a moderate negative relationship. With the exception of reliability coefficients, most correlations that we find in Psychology are small or moderate in size. The website http://rpsychologist.com/d3/correlation/ , created by Kristoffer Magnusson, provides an excellent interactive visualization of correlations that permits you to adjust the strength and direction of a correlation while witnessing the corresponding changes to the scatterplot.

Figure 6.4 Range of Pearson’s r, From −1.00 (Strongest Possible Negative Relationship), Through 0 (No Relationship), to +1.00 (Strongest Possible Positive Relationship)

There are two common situations in which the value of Pearson’s  r  can be misleading. Pearson’s  r  is a good measure only for linear relationships, in which the points are best approximated by a straight line. It is not a good measure for nonlinear relationships, in which the points are better approximated by a curved line. Figure 6.5, for example, shows a hypothetical relationship between the amount of sleep people get per night and their level of depression. In this example, the line that best approximates the points is a curve—a kind of upside-down “U”—because people who get about eight hours of sleep tend to be the least depressed. Those who get too little sleep and those who get too much sleep tend to be more depressed. Even though Figure 6.5 shows a fairly strong relationship between depression and sleep, Pearson’s  r  would be close to zero because the points in the scatterplot are not well fit by a single straight line. This means that it is important to make a scatterplot and confirm that a relationship is approximately linear before using Pearson’s  r . Nonlinear relationships are fairly common in psychology, but measuring their strength is beyond the scope of this book.

Figure 6.5 Hypothetical Nonlinear Relationship Between Sleep and Depression

The other common situations in which the value of Pearson’s  r  can be misleading is when one or both of the variables have a limited range in the sample relative to the population. This problem is referred to as  restriction of range . Assume, for example, that there is a strong negative correlation between people’s age and their enjoyment of hip hop music as shown by the scatterplot in Figure 6.6. Pearson’s  r  here is −.77. However, if we were to collect data only from 18- to 24-year-olds—represented by the shaded area of Figure 6.6—then the relationship would seem to be quite weak. In fact, Pearson’s  r  for this restricted range of ages is 0. It is a good idea, therefore, to design studies to avoid restriction of range. For example, if age is one of your primary variables, then you can plan to collect data from people of a wide range of ages. Because restriction of range is not always anticipated or easily avoidable, however, it is good practice to examine your data for possible restriction of range and to interpret Pearson’s  r  in light of it. (There are also statistical methods to correct Pearson’s  r  for restriction of range, but they are beyond the scope of this book).

Figure 6.6 Hypothetical Data Showing How a Strong Overall Correlation Can Appear to Be Weak When One Variable Has a Restricted Range.The overall correlation here is −.77, but the correlation for the 18- to 24-year-olds (in the blue box) is 0.

Correlation Does Not Imply Causation

You have probably heard repeatedly that “Correlation does not imply causation.” An amusing example of this comes from a 2012 study that showed a positive correlation (Pearson’s r = 0.79) between the per capita chocolate consumption of a nation and the number of Nobel prizes awarded to citizens of that nation [2] . It seems clear, however, that this does not mean that eating chocolate causes people to win Nobel prizes, and it would not make sense to try to increase the number of Nobel prizes won by recommending that parents feed their children more chocolate.

There are two reasons that correlation does not imply causation. The first is called the  directionality problem . Two variables,  X  and  Y , can be statistically related because X  causes  Y  or because  Y  causes  X . Consider, for example, a study showing that whether or not people exercise is statistically related to how happy they are—such that people who exercise are happier on average than people who do not. This statistical relationship is consistent with the idea that exercising causes happiness, but it is also consistent with the idea that happiness causes exercise. Perhaps being happy gives people more energy or leads them to seek opportunities to socialize with others by going to the gym. The second reason that correlation does not imply causation is called the  third-variable problem . Two variables,  X  and  Y , can be statistically related not because  X  causes  Y , or because  Y  causes  X , but because some third variable,  Z , causes both  X  and  Y . For example, the fact that nations that have won more Nobel prizes tend to have higher chocolate consumption probably reflects geography in that European countries tend to have higher rates of per capita chocolate consumption and invest more in education and technology (once again, per capita) than many other countries in the world. Similarly, the statistical relationship between exercise and happiness could mean that some third variable, such as physical health, causes both of the others. Being physically healthy could cause people to exercise and cause them to be happier. Correlations that are a result of a third-variable are often referred to as  spurious correlations.

Some excellent and funny examples of spurious correlations can be found at http://www.tylervigen.com  (Figure 6.7  provides one such example).

“Lots of Candy Could Lead to Violence”

Although researchers in psychology know that correlation does not imply causation, many journalists do not. One website about correlation and causation, http://jonathan.mueller.faculty.noctrl.edu/100/correlation_or_causation.htm , links to dozens of media reports about real biomedical and psychological research. Many of the headlines suggest that a causal relationship has been demonstrated when a careful reading of the articles shows that it has not because of the directionality and third-variable problems.

One such article is about a study showing that children who ate candy every day were more likely than other children to be arrested for a violent offense later in life. But could candy really “lead to” violence, as the headline suggests? What alternative explanations can you think of for this statistical relationship? How could the headline be rewritten so that it is not misleading?

As you have learned by reading this book, there are various ways that researchers address the directionality and third-variable problems. The most effective is to conduct an experiment. For example, instead of simply measuring how much people exercise, a researcher could bring people into a laboratory and randomly assign half of them to run on a treadmill for 15 minutes and the rest to sit on a couch for 15 minutes. Although this seems like a minor change to the research design, it is extremely important. Now if the exercisers end up in more positive moods than those who did not exercise, it cannot be because their moods affected how much they exercised (because it was the researcher who determined how much they exercised). Likewise, it cannot be because some third variable (e.g., physical health) affected both how much they exercised and what mood they were in (because, again, it was the researcher who determined how much they exercised). Thus experiments eliminate the directionality and third-variable problems and allow researchers to draw firm conclusions about causal relationships.

Key Takeaways

• Correlational research involves measuring two variables and assessing the relationship between them, with no manipulation of an independent variable.
• Correlation does not imply causation. A statistical relationship between two variables,  X  and  Y , does not necessarily mean that  X  causes  Y . It is also possible that  Y  causes  X , or that a third variable,  Z , causes both  X  and  Y .
• While correlational research cannot be used to establish causal relationships between variables, correlational research does allow researchers to achieve many other important objectives (establishing reliability and validity, providing converging evidence, describing relationships and making predictions)
• Correlation coefficients can range from -1 to +1. The sign indicates the direction of the relationship between the variables and the numerical value indicates the strength of the relationship.
• A cognitive psychologist compares the ability of people to recall words that they were instructed to “read” with their ability to recall words that they were instructed to “imagine.”
• A manager studies the correlation between new employees’ college grade point averages and their first-year performance reports.
• An automotive engineer installs different stick shifts in a new car prototype, each time asking several people to rate how comfortable the stick shift feels.
• A food scientist studies the relationship between the temperature inside people’s refrigerators and the amount of bacteria on their food.
• A social psychologist tells some research participants that they need to hurry over to the next building to complete a study. She tells others that they can take their time. Then she observes whether they stop to help a research assistant who is pretending to be hurt.

2. Practice: For each of the following statistical relationships, decide whether the directionality problem is present and think of at least one plausible third variable.

• People who eat more lobster tend to live longer.
• People who exercise more tend to weigh less.
• College students who drink more alcohol tend to have poorer grades.
• Bushman, B. J., & Huesmann, L. R. (2001). Effects of televised violence on aggression. In D. Singer & J. Singer (Eds.), Handbook of children and the media (pp. 223–254). Thousand Oaks, CA: Sage. ↵
• Messerli, F. H. (2012). Chocolate consumption, cognitive function, and Nobel laureates. New England Journal of Medicine, 367 , 1562-1564. ↵

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6 Correlational Design and Analysis

• Published: March 2018
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Interests in how variables may relate to each other and how systems of relationships among variables may be at play often underlie the questions music education researchers pose. This chapter describes basic design and analysis considerations in research that involves the systematic investigation of whether and how variables are related; in other words, correlational research. The chapter poses correlational research as an extension of the book’s previous discussion of descriptive research. The chapter briefly describes the role of correlational studies in advancing theory, presents several issues to consider when designing studies, and provides an introduction to correlation as a statistical concept.

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Correlation research: what is it and how can you use it.

11 min read If you want to find out if a new marketing campaign or product feature is connected to an increase in sales, correlation can help you determine if a relationship exists between those variables and whether there is a positive, negative or neutral impact.

What is correlation in research?

Correlation (often referred to as correlational study, correlation research, bivariate correlation or correlation analysis) is a core step in understanding your data (such as from survey research) or the relationship between variables in your dataset, typically expressed as x1 and x2.

If a correlation exists, one variable is correlated to another in a pairwise fashion.

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Measuring correlation

To measure the degree to which any two variables are correlated, we use a correlation coefficient (of which there are many).

A correlation coefficient is a statistical value, also known as Pearson’s Correlation Coefficient (or Pearson’s r), and is always between -1 and 1. Note: outliers can make coefficients look statistically significant but not meaningful or insightful.

Data points are plotted on a scatterplot and the shape of the data informs the researcher of the relationship between variables.

• -1 indicates a perfectly linear negative correlation
• 0 indicates no linear correlation
• 1 indicates a perfectly positive linear correlation

Negative correlation (or negative relationship)

A negative correlation is a relationship between two variables in which an increase in one variable is associated with a decrease in the other. For example, as you spend more money (increase) you save less (decrease).

Positive correlation (or positive relationship)

For positive correlation, both variables either increase or decrease at the same time. Let’s take hours worked versus money earned (assuming no set limit on working hours). As hours worked increases, so too does money earned.

What is a correlation matrix?

Once you’ve plotted your correlation coefficients for different variables, you can build a correlation matrix to display them (or use Stats iQ which can produce one for you). A correlation matrix essentially depicts the correlations between all possible pairs of values in a table. It’s an easy way to summarize large datasets and identify visual patterns across the relationships you are testing.

Relate capability in Stats iQ  

Relate explores the relationships between variables. When you select two variables and then select Relate, Stats iQ will choose the appropriate statistical test based on the structure of the data, run that test, then translate the results into a simple and clear explanation.

When you select three or more variables, Stats iQ will relate each variable to the one variable that has the key by it, then bring the strongest relationships to the top. You can select dozens of variables at a time, so you can sift through many relationships quickly.

Again, “Descriptive Frequencies” and “Bivariate Correlation” are basic steps that every data analyst should take before they move onto regression.

Note, a correlational analysis only provides information about variables at one specific point in time. The results could change if you repeat the study.

Furthermore, whilst a relationship may exist between variables, any change in one isn’t necessarily the cause of the change in the other. This brings us onto a basic rule and famous maxim: “Correlation does not imply causation.”

Correlation and causation

It’s a well-known saying that correlation doesn’t imply causation, but why?

Well, with correlation, nothing is constant — and this lack of control makes it impossible to determine cause and effect from a simple correlation study.

Correlation and causation exist at the same time, but “ causation ” is a much higher standard. For example, you find that your child is standing by a table and there’s milk all over the place. So they spilled it. No — the cat did it before you walked in the room.

Causation explicitly applies to time and prior relationships where an action causes an outcome. Put simply: it indicates that one event is the result of another.

Correlation, on the other hand, is simply a reflection of a relationship between two variables — when one changes, so does the other, but it’s not necessarily the cause. The only way to prove or demonstrate a causal relationship is through an appropriately designed and controlled experiment.

As such, there are two basic reasons why correlation doesn’t imply causation:

1. Directionality problem

The directionality problem refers to a possible relationship between two variables — that a change in one will result in a change in the other. This also implies that there’s a correlation between them. However, as correlation doesn’t imply causation, we cannot say with certainty that the change in one of the variables is the cause of the change in the other.

2. Latent variables

A latent variable is a variable that you can’t observe or measure — but you can detect them based on their effects on other observable variables. Consider the psychological construct of happiness or the idea of customer satisfaction: you can’t directly see these variables, but you can measure them indirectly using observed variables.

For example, cities with more grocery stores also tend to have higher crime rates. However, these two variables are only correlated because they have a high correlation with a third variable: population size.

Measuring latent variables

To measure latent variables, we use observed variables and then mathematically estimate the unseen variables. This involves using advanced statistical techniques like factor analysis, latent class analysis (LCA), structural equation modeling (SEM), and Rasch analysis. These techniques rely on the inter-correlations of variables.

The next step is multiple regression/correlation, then casual or predictive modeling. But more on these methods in another topic. So, why use correlation?

Why use correlation?

Correlation is an essential part of any research study as it helps you to understand the relationships between variables, and therefore form hypotheses as the next step of the process.

The advantages of using correlation in research are:

Results are likely to be more truthful to natural occurrences.

If no variables are influenced, then the variables are existing and interacting together as they would in ‘real life’, so the findings will be a true and accurate reflection of the variables.

It does identify variables with strong relationships

During statistical analysis of the data, correlational research will be able to indicate whether there is a positive or negative relationship, or no correlation at all, between the variables. This can be invaluable for research teams trying to identify the right variables to be concentrating future research on. Saves time and money

It can be time-consuming and costly to set up experiment conditions to test whether two variables interact with each other in a cause-and-effect way. correlational research provides a stepping-stone to show researchers the potential of variables in their natural setting, and perhaps bringing patterns to light that might not have been identified in the first place.

You should always use correlation in research, but you cannot always make inferences, because:

There is less external validity

If research findings cannot be repeated and are unable to provide conclusive results, because the observations were done in a natural setting where the variables were not isolated and may have been influenced by other factors.

Having a strong correlation does not infer causation

While two variables may be strongly connected, there cannot be a clear assessment of the cause-and-effect to provide a conclusion.

There is little control over the variables

It’s not possible to isolate the variables to confirm that only the two variables are being explored. There is always the possibility of the third variable.

No guarantee of the results not changing

If results are gathered that a researcher wants to replicate, the method of correlational research is backwards-looking, so there is no guarantee that the variable results won’t change in the future.

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By using a survey software technology platform to do your correlation analysis and research, you can save time analyzing your data yourself, and instead use the tool to conduct start-to-finish correlation analysis across the creation, data collection, analysis and reporting stages.

Qualtrics’ survey software streamlines your data collection methods and correlations, making it easy to access results, measure data trends, and uncover insights without the complexity or need to jump between systems.

What makes Qualtrics so different from other survey providers is that you can consult with trained research professionals, and it includes high-tech statistical software like Qualtrics Stats iQ ™. This can handle complicated analyses using these methods:

• Regression analysis – This is vital in correlational research as it measures the degree of influence of independent variables on a dependent variable (the relationship between two variables).
• Analysis of Variance (ANOVA) test – Commonly used with a regression study to find out what effect independent variables have on the dependent variable. It can compare multiple groups simultaneously to see if there is a relationship between them.
• Conjoint analysis – Asks people to make trade-offs when making decisions, then analyses the results to give the most popular outcome. Helps you understand why people make the complex choices they do.
• T-Test – Helps you compare whether two data groups have different mean values and allows the user to interpret whether differences are meaningful or merely coincidental.
• Crosstab analysis – Used in quantitative market research to analyze categorical data – that is, variables that are different and mutually exclusive, and allows you to compare the relationship between two variables in contingency tables.

If you want to learn how the system is set up for conducting and analyzing correlational research, try out a Qualtrics survey software demo to see how it works.

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Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Correlation Studies in Psychology Research

Determining the relationship between two or more variables.

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Brianna Gilmartin

• Characteristics

Potential Pitfalls

Frequently asked questions.

A correlational study is a type of research design that looks at the relationships between two or more variables. Correlational studies are non-experimental, which means that the experimenter does not manipulate or control any of the variables.

A correlation refers to a relationship between two variables. Correlations can be strong or weak and positive or negative. Sometimes, there is no correlation.

There are three possible outcomes of a correlation study: a positive correlation, a negative correlation, or no correlation. Researchers can present the results using a numerical value called the correlation coefficient, a measure of the correlation strength. It can range from –1.00 (negative) to +1.00 (positive). A correlation coefficient of 0 indicates no correlation.

• Positive correlations : Both variables increase or decrease at the same time. A correlation coefficient close to +1.00 indicates a strong positive correlation.
• Negative correlations : As the amount of one variable increases, the other decreases (and vice versa). A correlation coefficient close to -1.00 indicates a strong negative correlation.
• No correlation : There is no relationship between the two variables. A correlation coefficient of 0 indicates no correlation.

Characteristics of a Correlational Study

Correlational studies are often used in psychology, as well as other fields like medicine. Correlational research is a preliminary way to gather information about a topic. The method is also useful if researchers are unable to perform an experiment.

Researchers use correlations to see if a relationship between two or more variables exists, but the variables themselves are not under the control of the researchers.

While correlational research can demonstrate a relationship between variables, it cannot prove that changing one variable will change another. In other words, correlational studies cannot prove cause-and-effect relationships.

When you encounter research that refers to a "link" or an "association" between two things, they are most likely talking about a correlational study.

Types of Correlational Research

There are three types of correlational research: naturalistic observation, the survey method, and archival research. Each type has its own purpose, as well as its pros and cons.

Naturalistic Observation

The naturalistic observation method involves observing and recording variables of interest in a natural setting without interference or manipulation.  

Can inspire ideas for further research

Option if lab experiment not available

Variables are viewed in natural setting

Can be time-consuming and expensive

Extraneous variables can't be controlled

No scientific control of variables

Subjects might behave differently if aware of being observed

This method is well-suited to studies where researchers want to see how variables behave in their natural setting or state.   Inspiration can then be drawn from the observations to inform future avenues of research.

In some cases, it might be the only method available to researchers; for example, if lab experimentation would be precluded by access, resources, or ethics. It might be preferable to not being able to conduct research at all, but the method can be costly and usually takes a lot of time.  

Naturalistic observation presents several challenges for researchers. For one, it does not allow them to control or influence the variables in any way nor can they change any possible external variables.

However, this does not mean that researchers will get reliable data from watching the variables, or that the information they gather will be free from bias.

For example, study subjects might act differently if they know that they are being watched. The researchers might not be aware that the behavior that they are observing is not necessarily the subject's natural state (i.e., how they would act if they did not know they were being watched).

Researchers also need to be aware of their biases, which can affect the observation and interpretation of a subject's behavior.  

Surveys and questionnaires are some of the most common methods used for psychological research. The survey method involves having a  random sample  of participants complete a survey, test, or questionnaire related to the variables of interest.   Random sampling is vital to the generalizability of a survey's results.

Cheap, easy, and fast

Can collect large amounts of data in a short amount of time

Results can be affected by poor survey questions

Results can be affected by unrepresentative sample

Outcomes can be affected by participants

If researchers need to gather a large amount of data in a short period of time, a survey is likely to be the fastest, easiest, and cheapest option.  

It's also a flexible method because it lets researchers create data-gathering tools that will help ensure they get the information they need (survey responses) from all the sources they want to use (a random sample of participants taking the survey).

Survey data might be cost-efficient and easy to get, but it has its downsides. For one, the data is not always reliable—particularly if the survey questions are poorly written or the overall design or delivery is weak.   Data is also affected by specific faults, such as unrepresented or underrepresented samples .

The use of surveys relies on participants to provide useful data. Researchers need to be aware of the specific factors related to the people taking the survey that will affect its outcome.

For example, some people might struggle to understand the questions. A person might answer a particular way to try to please the researchers or to try to control how the researchers perceive them (such as trying to make themselves "look better").

Sometimes, respondents might not even realize that their answers are incorrect or misleading because of mistaken memories .

Archival Research

Many areas of psychological research benefit from analyzing studies that were conducted long ago by other researchers, as well as reviewing historical records and case studies.

For example, in an experiment known as  "The Irritable Heart ," researchers used digitalized records containing information on American Civil War veterans to learn more about post-traumatic stress disorder (PTSD).

Large amount of data

Can be less expensive

Researchers cannot change participant behavior

Can be unreliable

Information might be missing

No control over data collection methods

Using records, databases, and libraries that are publicly accessible or accessible through their institution can help researchers who might not have a lot of money to support their research efforts.

Free and low-cost resources are available to researchers at all levels through academic institutions, museums, and data repositories around the world.

Another potential benefit is that these sources often provide an enormous amount of data that was collected over a very long period of time, which can give researchers a way to view trends, relationships, and outcomes related to their research.

While the inability to change variables can be a disadvantage of some methods, it can be a benefit of archival research. That said, using historical records or information that was collected a long time ago also presents challenges. For one, important information might be missing or incomplete and some aspects of older studies might not be useful to researchers in a modern context.

A primary issue with archival research is reliability. When reviewing old research, little information might be available about who conducted the research, how a study was designed, who participated in the research, as well as how data was collected and interpreted.

Researchers can also be presented with ethical quandaries—for example, should modern researchers use data from studies that were conducted unethically or with questionable ethics?

You've probably heard the phrase, "correlation does not equal causation." This means that while correlational research can suggest that there is a relationship between two variables, it cannot prove that one variable will change another.

For example, researchers might perform a correlational study that suggests there is a relationship between academic success and a person's self-esteem. However, the study cannot show that academic success changes a person's self-esteem.

To determine why the relationship exists, researchers would need to consider and experiment with other variables, such as the subject's social relationships, cognitive abilities, personality, and socioeconomic status.

The difference between a correlational study and an experimental study involves the manipulation of variables. Researchers do not manipulate variables in a correlational study, but they do control and systematically vary the independent variables in an experimental study. Correlational studies allow researchers to detect the presence and strength of a relationship between variables, while experimental studies allow researchers to look for cause and effect relationships.

If the study involves the systematic manipulation of the levels of a variable, it is an experimental study. If researchers are measuring what is already present without actually changing the variables, then is a correlational study.

The variables in a correlational study are what the researcher measures. Once measured, researchers can then use statistical analysis to determine the existence, strength, and direction of the relationship. However, while correlational studies can say that variable X and variable Y have a relationship, it does not mean that X causes Y.

The goal of correlational research is often to look for relationships, describe these relationships, and then make predictions. Such research can also often serve as a jumping off point for future experimental research. 

Heath W. Psychology Research Methods . Cambridge University Press; 2018:134-156.

Schneider FW. Applied Social Psychology . 2nd ed. SAGE; 2012:50-53.

Curtis EA, Comiskey C, Dempsey O. Importance and use of correlational research .  Nurse Researcher . 2016;23(6):20-25. doi:10.7748/nr.2016.e1382

Carpenter S. Visualizing Psychology . 3rd ed. John Wiley & Sons; 2012:14-30.

Pizarro J, Silver RC, Prause J. Physical and mental health costs of traumatic war experiences among civil war veterans .  Arch Gen Psychiatry . 2006;63(2):193. doi:10.1001/archpsyc.63.2.193

Post SG. The echo of Nuremberg: Nazi data and ethics .  J Med Ethics . 1991;17(1):42-44. doi:10.1136/jme.17.1.42

Lau F. Chapter 12 Methods for Correlational Studies . In: Lau F, Kuziemsky C, eds. Handbook of eHealth Evaluation: An Evidence-based Approach . University of Victoria.

Akoglu H. User's guide to correlation coefficients .  Turk J Emerg Med . 2018;18(3):91-93. doi:10.1016/j.tjem.2018.08.001

Price PC. Research Methods in Psychology . California State University.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Importance and use of correlational research

Affiliation.

• 1 School of Nursing and Midwifery, Trinity College Dublin, Dublin, Republic of Ireland.
• PMID: 27424963
• DOI: 10.7748/nr.2016.e1382

Background: The importance of correlational research has been reported in the literature yet few research texts discuss design in any detail.

Aim: To discuss important issues and considerations in correlational research, and suggest ways to avert potential problems during the preparation and application of the design.

Discussion: This article targets the gap identified in the literature regarding correlational research design. Specifically, it discusses the importance and purpose of correlational research, its application, analysis and interpretation with contextualisations to nursing and health research.

Conclusion: Findings from correlational research can be used to determine prevalence and relationships among variables, and to forecast events from current data and knowledge. In spite of its many uses, prudence is required when using the methodology and analysing data. To assist researchers in reducing mistakes, important issues are singled out for discussion and several options put forward for analysing data.

Implications for practice: Correlational research is widely used and this paper should be particularly useful for novice nurse researchers. Furthermore, findings generated from correlational research can be used, for example, to inform decision-making, and to improve or initiate health-related activities or change.

Keywords: correlation; correlational research; data analysis; measurement tools; nurses; nursing research; quantitative; variables.

• Nursing Research*

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Interpretation of correlations in clinical research

1 University of Utah Department of Orthopaedic Surgery Operations, Salt Lake City, Utah, USA

Jerry Bounsanga

Maren wright voss, background:.

Critically analyzing research is a key skill in evidence-based practice and requires knowledge of research methods, results interpretation, and applications, all of which rely on a foundation based in statistics. Evidence-based practice makes high demands on trained medical professionals to interpret an ever-expanding array of research evidence.

As clinical training emphasizes medical care rather than statistics, it is useful to review the basics of statistical methods and what they mean for interpreting clinical studies.

We reviewed the basic concepts of correlational associations, violations of normality, unobserved variable bias, sample size, and alpha inflation. The foundations of causal inference were discussed and sound statistical analyses were examined. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias.

Recent published studies in the medical field provide evidence of causal assertion overreach drawn from correlational findings. The findings present a primer on the assumptions and nature of correlational methods of analysis and urge clinicians to exercise appropriate caution as they critically analyze the evidence before them and evaluate evidence that supports practice.

Conclusion:

Critically analyzing new evidence requires statistical knowledge in addition to clinical knowledge. Studies can overstate relationships, expressing causal assertions when only correlational evidence is available. Failure to account for the effect of sample size in the analyses tends to overstate the importance of predictive variables. It is important not to overemphasize the statistical significance without consideration of effect size and whether differences could be considered clinically meaningful.

Introduction

It is common for physicians to underestimate the relevance and importance of their training in statistics and probability, at least until its relevance becomes clear in later clinical practice. 1 Evidence based practice is the standard of care, yet evaluating the quality of evidence can be a difficult process. The British Medical Journal established clear statistical guidelines for contributions to medical journals in the 1980’s. 2 A survey of medical residents showed near perfect agreement (95%) that understanding statistics they encounter in medical journals is important, but 75% said they lacked knowledge. 3 The average score on the basic biostatistics exam for the residents surveyed was 41% correct, showing both the objective and subjective need for a stronger statistical training foundation. An international survey of practicing doctors similarly found that doctors averaged scores of 40% when tested on the basics of statistical methods and epidemiology. 4

The efforts to improve the methods behind clinical science are ongoing. Consolidated Standards of Reporting Trials (CONSORT) have been established, 5 along with multiple other research and statistical method guidelines in recent years. 6 These guidelines are updated periodically 7 and endorsed or extended by specific clinical medical groups, 8 , 9 or medical journals. 10 – 13 Key to many of these guidelines are attempts to ensure that bias and error are minimized in research, ensuring interpretations are meaningful and accurate. It would not be practical to detail an exhaustive list of guidance and recommendations. However, with new information and research approaches constantly coming forward, physicians must have the ability to critically evaluate the quality of evidence presented. Critically analyzing research is a key skill in evidence based practice and requires knowledge of methods, results interpretation, and applicability—all three of which require an understanding of basic statistics. 14

The intention of this article is to serve as a basic primer regarding critical statistical concepts that appear in medical literature with a focus on the concept of correlation and how it is best utilized in clinical interpretation for understanding the relationships between health factors. At its foundation, correlational analysis quantifies the direction and strength of relationships between two variables. Understanding correlations can form the basis for interpreting applications of clinical research.

1. What Does Correlation Tell Us?

Correlation is concerned with association; it can look at any two measured concepts and compare their relationships. These measured concepts are often referred to as variables and are assigned letter labels (X, Y). Thus, the correlation is the measure of the relationship between X and Y, and it ranges from −1 to 1. Its value (or coefficient) is scaled within this range to assist in interpretation, with 0 indicating no relationship between variables X and Y, and −1 or 1 indicating the ability to perfectly predict X from Y or Y from X (see Figure 1 ). A correlation coefficient provides two pieces of information. First, it predicts where X (the measured value of interest) falls on a line given a known value of Y. Second, it expresses a reduction in variability associated with knowing Y, telling us something about the expected range of the X value. 15 Correlation takes into account the full range of scores, but as a statistical tool it is not very sensitive to scores on the very high or very low ends of our X value. The common Pearson correlation is best used to describe linear relationships. If the pattern of association between the two variables is, for example, a “U” shaped curve, the correlation results might be low, even though a defined relationship exists. 16

1) Perfect negative correlation between two variables; 2) No patterned relationship between 2 variables; 3) Perfect positive correlation between two variables

Correlation is not easily impacted by skewed or off-center data results. The nature of the data can be described by its parameters, like measures of central tendency, which inform us about the distribution of the data. Parametric tests make assumptions, such as the data are normally distributed, while non-parametric tests are called distribution-free tests because they make no assumptions about the distribution of the data. Yet even if the pattern of scores are not in a normal bell shaped curve or they don’t create a direct linear relationship, correlations can still be reliable. A number of correlation measures have been developed to handle different types of data (non-parametric tests like the kendall rank, spearman rank correlation, phi correlation, biserial correlation, point-biserial correlation and gamma correlation). Yet even the simple Pearson correlation handles extreme violations of normality (no bell shape to the pattern of scores) and scale. The Pearson correlation was tested by randomly drawing 5,000 small samples (n=5 to n=15) from a population of 10,000 to calculate the distribution of r values yielded (small samples might challenge parametric assumptions), and was still found to be a reliable indicator of the relationship between variables. 17 It is also possible to use transformations to normalize the distribution of data. Correlation can be a robust measure, in part from its ability to tolerate these violations of normally distributed data while staying sensitive to the individual case. The properties of correlation make the technique useful in interpreting the meaning derived from clinical data.

2. Use of Correlation

There are many concerns with the statistical techniques that are commonly utilized in the literature. Some concerns arise from a misunderstanding of the statistical measure and others from its misapplication. We discuss four ways in which correlational analysis is misused, including causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias. Importantly, correlation is a measure of association, which is insufficient to infer causation. Correlation can only measure whether a relationship exists between two variables, but it does not indicate causal relationship. There must be a convincing body of evidence to take the next step on the path to inferring that one variable causes the other. Randomized controlled trials or more advanced statistical methods such as path analysis and structural equation modeling, coupled with proper research design, are needed in order to take the next step of inference in the causal chain, testing a causal hypothesis. While correlational analyses are by definition from non-experimental research, research without carefully controlled experimental conditions, it is nevertheless relevant to evidence-based practice. 18 Observational methods of study can be conducted using either a cross-sectional design (a snap-shot of prevalence at one time-point), a retrospective design (looking back to compare current with past attributes) or prospective design (documenting current occurrences and following up at a future time-point to make comparisons). Correlations drawn from cross-sectional studies cannot establish the temporal relationship that links cause with effect, yet adding a retrospective or prospective observational design provides additional strength to the association and helps support hypothesis generation to then later test the causal assertions with a different research design. 19

When non-experimental methods are used, it means the relationship seen between the two variables is vulnerable to bias from anything that was not measured (unobserved variables). Whether studying pain or function or treatment response, there are a host of possible factors that might be important to the observed correlational relationship between X and Y. Any given study has only measured and reported on a fraction of the potential variables of impact. These unobserved variables could potentially explain the observed relationship, so it would be premature to assume a treatment effect based on correlational data. The unobserved variables might be affecting the study variables, changing the relationship in a way that might alter the interpretation of the data. Thus interpretation of correlational findings must be quite cautious until further research is completed.

Another concern is the use (or abuse) of the term ‘statistically significant’ in correlational analysis. This concern is not new. The abuse of significance testing was noted in a 1987 review published in the New England Journal of Medicine. That article found that a number of components in clinical trials, such as having several measures of the outcome (i.e., multiple tests of function, health, or pain), repeated measures over time, including subgroup analyses, or multiple treatments in the same trial, can lead to a bias in reporting which exaggerates the size or importance of observed differences. 20 It is natural for researchers to want to thoroughly evaluate the potential difference between treatments conditions. This has been sometimes referred to as the kitchen sink approach and it presents a problem for using significance tests. Significance as a statistical procedure addresses the question of the probability of the hypothesized occurrence. If the probability ( p ) is less than say 5% or 1%, the researcher might feel comfortable making the assumption that the observed event was not due to chance. The 0.05 significance value was original proposed by Sir Ronald Fisher in 1925, but the 0.05 value was never intended as a hard and fast rule. 21 If researchers use a cut-off of p=0.05 to determine whether the effect they see in their research has occurred randomly by chance, running multiple tests can quickly move the needle from a rare to an expected event. Every analysis run with a p =0.05 alpha criteria yields a 5% chance that the “significant” finding is actually a chance occurrence, called a Type 1 error.

So we can see the difficulty that occurs when running 20 different analyses on the same data. This would produce a 64% chance that a significant p -value will show up erroneously, when there is no systematic relationship, and it is really just a chance occurrence (see Figure 2 ). This over analysis of the data can possibly create an interpretation that a test or treatment should be used when there was no actual treatment effect. This is the problem of alpha-inflation and it needs to be carefully considered both in conducting and interpreting correlational research. It can be corrected by planning ahead for the analyses that will be run and keeping them limited to key theoretical questions. 20 Additionally, alpha can be adjusted in the statistical calculation, for example with the Bonferroni correction, a procedure used to reduce alpha and statistically correct for the inflation created by multiple comparisons. It is performed by simply dividing the alpha value (α) by the number of hypotheses or measures ( m ) tested. If the study wanted to evaluate 5 different surgical placements, using the Bonferroni correction would adjust an original α=0.05 by applying the formula, α/ m or 0.05/5, yielding α=0.01, a stricter standard before a study finding would be considered significant. A Bonferroni correction is a conservative correction for multiple comparisons that reduces the Type 1 error, 22 though less conservative alternatives exist like the Tukey or the Holm-Sidak corrections. The main idea is that clinicians acknowledge the problem of multiple comparisons made in a single study and address the concern so that spurious relationships are not erroneously reported as significant. A lack of awareness about this issue can lead to naïve interpretations of study findings.

As the number of comparisons increases, the alpha error rate increases; running 20 comparisons with no correction factor and a significance level set at 0.05 will result in 64% chance of a relationship appearing to be significant (better than 50/50 odds).

Exaggeration of significance testing leads to a third point - are the findings clinically meaningful? A significant finding does not infer a meaningful finding. This is because factors other than variance in scores influence the p -value or significance in a correlational analysis. Sample size is an important element in whether a non-random effect will be found. Small sample sizes might produce unstable, but significant, correlation estimates, so sample sizes greater than 150 to 200 have been recommended. 23 Yet, it is not uncommon for published papers to report significant effects through correlational analysis of sample sizes of less than 150 patients. 24 – 26 While reporting and publishing both the significant and non-significant results are important, given the instability that comes from a small sample size, there should be caution taken with interpretation until replication studies can verify the findings.

Likewise, large samples can also be problematic. A large sample might reveal a statistically significant difference between groups, but its effect might be minimal. In a classic example, a sample of 22,000 subjects showed a highly significant ( p <.00001) reduction in myocardial infarctions that prompted a general recommendation to take aspirin for myocardial infarction prevention. 27 The effect size, however, was less than a 1% reduction in risk, such that the risk of taking aspirin exceeded the benefit. Effect size is the standardized mean difference between groups and is a measure of the magnitude of between group differences. A significant p -value indicates only that a difference exists with no indication of size of the effect. Additionally, a confidence interval (CI) can be constructed for the effect size. CIs present a lower and upper range where the true population value is most likely to lie. 28 If a zero value is not included within the CI of the effect size, we have added assurance that the effect exists, with the size of the CI helpful in estimating the size of the effect. 29 It has been recommended that effect sizes or confidence intervals be included in all reported medical research so that the clinical significance of findings can be assessed. 20 , 28 , 30

3. Proper Interpretation of Correlation

Correlational analyses have been reported as one of the most common analytic techniques in research at the beginning of the 21 st century, particularly in health and epidemiological research. 15 Thus effective and proper interpretation is critical to understanding the literature. Cautious interpretation is particularly important, not only due to the interpretive concerns just detailed (causal inference overreach, over-reliance on significance, alpha inflation, and sample size bias), but also given the publication bias of journals to accept and publish studies with positive findings. 31 If clinicians are less likely to be exposed to under-published contradictory reports, based on null findings that treatments actually had no effect, the interpretation of the positive results must necessarily be cautious until confirmed through strong evidence.

One recent clinical example of correlational findings is an inference that because Cobb angle and sagittal balance are related to symptom severity in back pain, treatments should be aimed to improve sagittal balance. Studies used to draw these conclusions were making an important first step in identifying potentially relationships, but were not conclusive as they did not establish causal relationships, did not report effect sizes, and did not include control groups in the analyses. 32 – 34 The Pearson correlation or the Spearman correlation are tools that predict X from Y or Y from X. The nature of the correlation is symmetric, so that if the variables are inferred from a reversed direction (pain predicting spine function rather than spine function predicting pain), the same prediction holds true. 15 If one is looking for cause and effect, the correlational statistics cannot help. The mathematics of correlation tells us that Y is just as likely to precede X as to come after, because the prediction is the same regardless of which variable is inputted first. Effects cannot be determined directly through correlational analysis and perhaps the reverse relationship is the true relationship.

Because of the possibility of a bidirectional relationship, causal inference will be premature if relying purely on correlational statistics, no matter how many studies report the correlational finding. Correlation can be interpreted as the association between two variables. It cannot be used to indicate causal relationship. In fact, statistical tests cannot prove causal relationships but can only be used to test causal hypotheses. Misinterpretation of correlation is generally related to a lack of understanding of what a statistical test can or cannot do, as well as lacking knowledge in proper research design. Rather than jumping to an assumption of causality, the correlations should prompt the next stage of clinical research through randomly controlled clinical trials or the application of more complex statistical methods such as causal and path analysis. Perhaps a part of the tendency to jump too quickly to causal assertion arises from the nature of the questions asked in clinical research and the desire to quickly move to enhance patient care. New frameworks are emerging in the health sciences that challenge the appeal to a single cause by considering potential outcomes in a more complex ways. 35 Until then, understanding the nature of correlational analysis allows clinicians to be more cautious in interpreting study results.

Advances in research have led to many significant findings that are shaping how we diagnose and treat patients. As these findings might guide surgeons and clinicians into new treatment directions, it is important to consider the strength and nature of the research. Critically analyzing new evidence requires understanding of research methods and relevant statistical applications, all of which require an understanding of the analytic methodologies that lie behind the study findings.14 Evidence based practice is demanding new skills of trained medical professionals as they are presented with an ever-expanding array of research evidence. This short primer on theassumptions and nature of correlational methods of analysis can assist emerging physicians in understanding and exercising the appropriate caution as they critically analyze the evidence before them.

Acknowledgments

This investigation was supported by the University of Utah Department of Orthopaedics Quality Outcomes Research and Assessment, Study Design and Biostatistics Center, with funding in part from the National Center for Research Resources and the National Center for Advancing Translational Sciences, National Institutes of Health, through Grant 5UL1TR001067–02.

Declaration of Interests

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

• Open access
• Published: 24 May 2024

Association between characteristics of employing healthcare facilities and healthcare worker infection rates and psychosocial experiences during the COVID-19 pandemic

• Jay B. Lusk 1 , 2 ,
• Pratik Manandhar 3 ,
• Laine E. Thomas 3 &
• Emily C. O’Brien 2 , 3  

BMC Health Services Research volume  24 , Article number:  659 ( 2024 ) Cite this article

65 Accesses

Metrics details

Healthcare facility characteristics, such as ownership, size, and location, have been associated with patient outcomes. However, it is not known whether the outcomes of healthcare workers are associated with the characteristics of their employing healthcare facilities, particularly during the COVID-19 pandemic.

This was an analysis of a nationwide registry of healthcare workers (the Healthcare Worker Exposure Response and Outcomes (HERO) registry). Participants were surveyed on their personal, employment, and medical characteristics, as well as our primary study outcomes of COVID-19 infection, access to personal protective equipment, and burnout. Participants from healthcare sites with at least ten respondents were included, and these sites were linked to American Hospital Association data to extract information about sites, including number of beds, teaching status, urban/rural location, and for-profit status. Generalized estimating equations were used to estimate linear regression models for the unadjusted and adjusted associations between healthcare facility characteristics and outcomes.

A total of 8,941 healthcare workers from 97 clinical sites were included in the study. After adjustment for participant demographics, healthcare role, and medical comorbidities, facility for-profit status was associated with greater odds of COVID-19 diagnosis (aOR 1.76, 95% CI 1.02–3.03, p  = .042). Micropolitan location was associated with decreased odds of COVID-19 infection after adjustment (aOR = 0.42, 95% CI 0.24, 0.71, p  = .002. For-profit facility status was associated with decreased odds of burnout after adjustment (aOR = 0.53, 95% CI 0.29–0.98), p  = .044).

Conclusions

For-profit status of employing healthcare facilities was associated with greater odds of COVID-19 diagnosis but decreased odds of burnout after adjustment for demographics, healthcare role, and medical comorbidities. Future research to understand the relationship between facility ownership status and healthcare outcomes is needed to promote wellbeing in the healthcare workforce.

Trial registration

The registry was prospectively registered: ClinicalTrials.gov Identifier (trial registration number) NCT04342806, submitted April 8, 2020.

Peer Review reports

The COVID-19 pandemic added a major stressor to the already overtaxed healthcare system in the United States. Existing trends toward health system consolidation and from independent practice were exacerbated by the financial impact of the pandemic [ 1 , 2 , 3 ]. Prior work has demonstrated a consistent impact of healthcare facility characteristics on care quality, with variation in patient outcomes according to for-profit status, bed size, and teaching status; consolidation of health systems has also been linked with worsened patient outcomes [ 4 , 5 , 6 ].

There is also a robust literature relating health facility characteristics with the experiences of healthcare workers who work in those facilities. Several dimensions of the healthcare workplace are associated with outcomes for healthcare professionals. For example, there is an extensive literature demonstrating that physical environment (comprising items ranging from noise levels to temperature to ergonomics) is associated with workplace satisfaction and healthcare worker wellbeing [ 7 ]. Work environments that promote work engagement and self-efficacy are associated with job satisfaction and decrease turnover; by contrast, poor management, unprofessional behavior from colleagues, and unfavorable clinical work structures are associated with decreased job satisfaction and increased turnover [ 8 , 9 , 10 ]. Other workplace characteristics, such as high patient volumes, working a high burden of night shift work, and having a principally academic practice have been associated with increased burnout [ 11 ].

The operating characteristics of healthcare facilities may have a major influence on their ability to provide positive workplace environments for healthcare workers. For example, a prior study showed that registered nurses working in small hospitals were more likely to report job satisfaction than nurses working in large hospitals [ 12 ]. This same study found no association between employment in an academic hospital or facility location in a high population density area and satisfaction with the work environment, and no association with any of the above features and burnout after adjustment for age, sex, and level of education [ 12 ]. On the converse, a study of physical and occupational therapists found that employment in smaller healthcare facilities was associated with higher job stress and burnout levels [ 13 ]. A systematic review and meta-analysis of burnout by inpatient versus outpatient work environment of physicians found that outpatient physicians reported more emotional exhaustion than inpatient physicians, but otherwise did not find compelling evidence of differences in other psychosocial outcomes [ 14 ].

Despite the robust literature exploring the impact of employing healthcare facilities on outcomes of healthcare workers, this has not been explored in a large, diverse cohort of healthcare workers in the context of the COVID-19 pandemic. Therefore, our objective was to characterize the associations between healthcare facility characteristics and healthcare worker outcomes, including burnout, depression, and COVID-19 exposure and diagnosis, among participants of the Healthcare Worker Exposures, Response, and Outcomes (HERO) registry.

The registry and this analysis (ClinicalTrials.gov Identifier NCT04342806) was funded by the Patient-Centered Outcomes Research Institute (PCORI) and was approved by the WIRB-Copernicus Group Institutional Review Board (WCG IRB). Characteristics of the registry and assessment tools have been described previously [ 15 , 16 ]. Written informed consent was obtained from all participants. This study adhered to the tenets of the Declaration of Helsinki. We presented results according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement. We analyzed data from participants enrolled in the HERO registry from April 2020-May 2022. Participants who did not provide information on their employing healthcare facility, or whose employing healthcare facility could not be linked to American Hospital Association database were excluded. Participants from any site with more than 10 healthcare workers (HCWs) enrolled were included, for a final cohort of 8,941 HCWs from 97 clinical sites. Exposures of interest included healthcare facility characteristics, namely bed size, teaching status, urban location, and for-profit status, obtained from American Hospital Association data. We evaluated associations between these characteristics and four outcomes of interest prioritized by HCW participants during the COVID-19 pandemic. These included 1) access to personal protective equipment (PPE) quantified on a summary scale from ten questionnaires asking about various surrogates of access to PPE, 2) COVID-19 diagnosis on at least one survey, 3) burnout (defined as responding to at least three burnout symptoms on a burnout instrument on at least one occasion), and 4) depression (defined as a PROMIS-T score > 60 on at least one survey). Generalized Estimating Equations—to account for within-site clustering— were used to estimate linear regression models for the access to PPE outcome, and to estimate logistic regression models for the other outcomes. Adjusted regression models included covariates for age, gender, race, ethnicity, role in healthcare setting (e.g. nurse), healthcare environment (e.g. outpatient), and self-reported medical comorbidities. Dependent and independent variables used in our study are described in detail in Appendix 1 . Median values were used to impute missing continuous adjustment variables and mode values were used to impute missing categorical adjustment variables. Around 10% of participants were missing information on self-reported medical history, and < 1% of all other variables were missing. The vast majority of missing data on self-reported medical history was missing completely at random due to a version change in the data collection form. Imputation was only performed on missing adjustment variables and not on study endpoints. Unadjusted and adjusted odds ratios with 95% confidence intervals and p-values were estimated.

Characteristics of the study population

Characteristics of included participants are shown in Table  1 . The median age was 40 (IQR 33–51), 77.2% of the participants were female, 86.8% identified as White, 4.3% identified as Black or African American, and 8.9% identified as another race; 6.2% of participants identified as Hispanic ethnicity. Most participants worked in inpatient settings (80.2%); 9.2% worked in outpatient settings, 1.2% in emergency services, skilled nursing, or urgent care, and 9.4% in other healthcare settings. Nurses made up a plurality of the participants (32.3%) followed by physicians (21.9%), administrative staff (7.3%), and physicians assistants or nurse practitioners (6.2%).

Characteristics of the study population stratified by characteristics of employing healthcare facilities

Baseline characteristics of the participants varied according to hospital characteristics. Table 2 shows baseline characteristics stratified by hospital ownership status. In summary, for-profit and not-for-profit sites had a somewhat higher proportion of workers who identified as non-Hispanic compared to government sites; furthermore, a greater share of respondents from not-for-profit hospitals were physicians or physicians in training.

Table 3 shows baseline characteristics of the study population stratified by metropolitan versus micropolitan location; participants employed by facilities in micropolitan locations tended to be older, were more likely to identify as White and less likely to identify as Hispanic, were more likely to report employment in facilities other than hospitals and were more likely to be employed as medical assistants or medical administrators rather than physicians or registered nurses.

Table 4 shows baseline characteristics of the study population stratified by teaching status of the employing healthcare facility. Overall, participants from teaching institutions were younger, were less likely to identify as White and more likely to identify as Hispanic, were more likely to work in inpatient settings, and were more likely to be employed as physicians or nurses.

Table 5 shows baseline characteristics of the study population stratified by number of beds of the affiliated hospital. Overall, participants from smaller hospitals tended to be older, were more likely to identify as White and less likely to identify as Hispanic and were less likely to be employed as physicians compared with participants from larger hospitals.

Association between healthcare facility characteristics and access to personal protective equipment

There was no association either before or after adjustment (for age, gender, race, ethnicity, role in healthcare setting (e.g. nurse), healthcare environment (e.g. outpatient), and self-reported medical comorbidities) between any healthcare facility characteristic (ownership status, teaching status, number of beds, and metropolitan vs. micropolitan location) and access to personal protective equipment (Table  6 ).

Association between healthcare facility characteristics and COVID-19 diagnosis

Before adjustment, only for-profit ownership of the employing healthcare facility was associated with COVID-19 diagnosis (OR 1.94, 95% CI 1.26–3.01). After adjustment, this association was attenuated but was still significant (aOR 1.76, 95% CI 1.02–3.03). Furthermore, while before adjustment there was no association between micropolitan location of the employing healthcare facility and COVID-19 diagnosis (OR 0.79, 95% CI 0.46–1.38), after adjustment, healthcare workers employed by facilities located in micropolitan areas had lower adjusted odds of COVID-19 diagnosis (aOR 0.42, 95% CI 0.24–0.71). There was no association between number of beds or teaching status and COVID-19 diagnosis either before or after adjustment (Table  6 ).

Associations between healthcare facility characteristics and burnout and depressive symptoms

Before adjustment, no healthcare facility characteristic was associated with burnout. However, after adjustment, for-profit ownership of the employing healthcare facility was associated with decreased odds of burnout (aOR 0.53, 95% CI 0.29–0.98) (Table  6 ). Before adjustment, participants employed by healthcare facilities in micropolitan areas had decreased odds of experiencing depressive symptoms (OR 0.80, 95% CI 0.66–0.98); however, after adjustment this association was no longer observed (aOR 0.67, 95% CI 0.43–1.04).

In this large, nationwide, longitudinal patient-reported outcomes study of nearly 9,000 healthcare workers, we found that working in a for-profit healthcare facility was associated with 76% greater odds of COVID-19 infection but 43% decreased odds of reporting burnout after controlling for demographics, role in the workplace, type of healthcare facility, and comorbid condition burden. We also found that working at a healthcare facility in a micropolitan area vs metropolitan area was associated with 68% decreased odds of COVID-19 infection.

Our study provides important information for public health systems. During infectious disease epidemics, health system resilience (defined as the ability of health systems to resist and adapt to external threats) is of paramount importance, and one critical dimension of health system resilience is having sufficient staffing to effectively run critical health services [ 17 , 18 ]. Healthcare facilities vary substantially in their operational models, particularly in the United States, where the complex patchwork of reimbursement structures and concordant incentives for healthcare facilities results in sometimes dramatic differences in operational approaches [ 19 , 20 , 21 ]. Understanding how these different operational models intersect with experiences of healthcare workers is of paramount importance to developing effective public health approaches to pandemic preparedness [ 22 , 23 , 24 ].

Our study also has important implications for health system performance beyond infectious disease outbreaks. The literature exploring outcomes, especially health and psychosocial outcomes, of healthcare workers and how these outcomes vary according to the operational models of the facility by which they are employed is unfortunately sparse [ 25 , 26 , 27 , 28 ]. Future research is critically needed to understand how healthcare workers’ experiences may be affected by operational strategies used by their employing healthcare facilities. Given the incredibly high rates of burnout among patient facing staff in United States healthcare facilities and workforce shortages that are expected to continue to worsen in coming years, identifying whether particular operational models or workplace structures are particularly associated with adverse psychosocial outcomes for healthcare workers may allow policymakers and hospital administrators to identify the most effective targets for areas of intervention to improve retention and limit burnout among clinical staff [ 29 , 30 ].

There are a broad range of possible explanations for the associations observed in this study. With regard to for-profit versus non-profit status, it is possible that for-profit healthcare facilities were less likely to support healthcare worker adherence to COVID-19 prevention strategies, perhaps related to decreased staffing levels, which prior studies have shown are more common in for-profit medical facilities [ 31 , 32 ]. Furthermore, prior studies have shown that for-profit facilities in the United States experienced greater levels of financial instability than non-profit facilities, which could have resulted in increased strain on facility and staff resources leading to greater COVID-19 infection rates [ 33 , 34 ].

The decreased rate of burnout seen in for-profit facilities is not consistent with prior studies before the COVID-19 pandemic. For example, a study in Sweden showed that burnout levels were the highest at a private, for-profit hospitals compared to a publicly administered hospital [ 35 ]. Furthermore, prior studies in the United States have shown that for-profit nursing homes tended to have worse results with regard to employee wellbeing [ 36 ]. However, these studies were conducted before the COVID-19 pandemic, so it is unclear the impact the pandemic may have had on burnout outcomes. It is also possible that our results are a result of unmeasured selection bias, given that the HERO study advertised participation most prominently in not-for-profit academic medical centers, meaning that participants from for-profit facilities may have been those with the most emotional reserve to participate in survey efforts beyond their work and therefore may have been less likely to report burnout.

Regarding the lower rate of COVID-19 diagnoses observed in healthcare workers employed by facilities located in micropolitan vs. metropolitan environments, it is possible that the underlying spread of COVID-19 during the study period may have been most prominent in metropolitan areas. Prior studies showed that in the first 5 months of the COVID-19 pandemic (during which the HERO study enrolled most participants), the incidence rates of COVID-19 cases were higher in metropolitan areas; incidence rates in non-metropolitan areas overtook incidence rates in metropolitan areas in approximately August 2020 [ 37 ]. Another study demonstrates that a much smaller number of micropolitan counties were classified as COVID-19 hotspots than metropolitan counties, providing further support to this interpretation [ 38 ].

Our study has limitations. One key limitation of our study is its reliance on self-reported data with risk for selection bias. Furthermore, our study only included sites where at least 10 healthcare workers responded to the survey, which systematically excluded the smallest sites, which plausibly could be systematically different than larger sites and could introduce a risk of bias. Furthermore, the HERO registry was coordinated through several large academic medical centers and therefore likely over-represents participants from academic medical centers. Our study also relies on American Hospital Association data to perform linkage, which, while effective at identifying hospitals and health systems, may not be as effective at identifying clinics, especially those that are independent of larger health systems. However, while these limitations may limit the generalizability of our study, our results are nonetheless meaningful for policymakers studying the healthcare facilities where most healthcare workers are employed. Another limitation of our study is our inability to control for local rates of COVID-19 cases at each presenting hospital, which could conceivably be a surrogate for the stress on local health systems. County-level data is insufficient for such a purpose, as the case rates at each presenting healthcare facility may not be closely related to the number of cases in the county the facility is located in. Estimating and evaluating variation in COVID-19 caseload at the level of individual healthcare facilities could be an important direction for future research. Furthermore, there are a variety of causal pathways that could connect our endpoints with each other (e.g. lack of access to PPE leading to COVID-19 infection leading to burnout leading to depression, or burnout leading to COVID-19 infection [through decreased use of appropriate PPE, if available]). Future studies should explicitly evaluate these questions using appropriate causal inference strategies.

Strengths of our study include its particularly large size (the HERO registry is the largest of its kind in the United States), reliance on participant reported outcomes that are directly relevant to healthcare workers, and our ability to adjust for key characteristics of healthcare workers such as their role in the healthcare workplace, age, and self-reported medical comorbidities.

In summary, our work provides important preliminary data assessing the impact of healthcare facility structure and operational characteristics on healthcare worker outcomes during infectious disease pandemics. Future studies to carefully track the outcomes of healthcare workers and the association between these outcomes and operational characteristics of the facilities that employ them are urgently needed to inform health policy. Future analyses of the mechanisms of the association between healthcare facility characteristics and outcomes are needed to inform strategies to promote resilience against future pandemics and to promote stability and limit burnout in the healthcare workforce. Policymakers should be aware of the potential association between health system structure and healthcare facility characteristics and healthcare worker outcomes during an infectious disease pandemic.

Availability of data and materials

The data that support the findings of this study are available from the HERO registry, but restrictions apply to the availability of these data and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the HERO Registry oversight committee. Please contact [email protected] to request access to the data.

Abbreviations

Personal protective equipment

Healthcare worker exposure, response, and outcomes

Healthcare worker

Patient-centered outcomes research institute

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Acknowledgements

The authors acknowledge the participants in the HERO registry and the staff and clinical advisors who have supported the HERO research program.

This study was funded by the Patient Centered Outcomes Research Institute (PCORI). The study sponsor had no role in study design, collection, analysis, or interpretation of data, writing the manuscript, or the decision to submit the work for publication.

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Jay B. Lusk: Conceptualization, investigation, methodology, writing-original draft; Pratik Manandhar: methodology, formal analysis, data curation, writing-review and editing; Laine E. Thomas: methodology, formal analysis, data curation, writing- review and editing; Emily C. O’Brien: conceptualization, data curation, investigation, funding acquisition, supervision, writing-review and editing.

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Lusk, J.B., Manandhar, P., Thomas, L.E. et al. Association between characteristics of employing healthcare facilities and healthcare worker infection rates and psychosocial experiences during the COVID-19 pandemic. BMC Health Serv Res 24 , 659 (2024). https://doi.org/10.1186/s12913-024-11109-6

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McKinsey Global Private Markets Review 2024: Private markets in a slower era

At a glance, macroeconomic challenges continued.

McKinsey Global Private Markets Review 2024: Private markets: A slower era

If 2022 was a tale of two halves, with robust fundraising and deal activity in the first six months followed by a slowdown in the second half, then 2023 might be considered a tale of one whole. Macroeconomic headwinds persisted throughout the year, with rising financing costs, and an uncertain growth outlook taking a toll on private markets. Full-year fundraising continued to decline from 2021’s lofty peak, weighed down by the “denominator effect” that persisted in part due to a less active deal market. Managers largely held onto assets to avoid selling in a lower-multiple environment, fueling an activity-dampening cycle in which distribution-starved limited partners (LPs) reined in new commitments.

About the authors

This article is a summary of a larger report, available as a PDF, that is a collaborative effort by Fredrik Dahlqvist , Alastair Green , Paul Maia, Alexandra Nee , David Quigley , Aditya Sanghvi , Connor Mangan, John Spivey, Rahel Schneider, and Brian Vickery , representing views from McKinsey’s Private Equity & Principal Investors Practice.

Performance in most private asset classes remained below historical averages for a second consecutive year. Decade-long tailwinds from low and falling interest rates and consistently expanding multiples seem to be things of the past. As private market managers look to boost performance in this new era of investing, a deeper focus on revenue growth and margin expansion will be needed now more than ever.

Perspectives on a slower era in private markets

Global fundraising contracted.

Fundraising fell 22 percent across private market asset classes globally to just over $1 trillion, as of year-end reported data—the lowest total since 2017. Fundraising in North America, a rare bright spot in 2022, declined in line with global totals, while in Europe, fundraising proved most resilient, falling just 3 percent. In Asia, fundraising fell precipitously and now sits 72 percent below the region’s 2018 peak.

Despite difficult fundraising conditions, headwinds did not affect all strategies or managers equally. Private equity (PE) buyout strategies posted their best fundraising year ever, and larger managers and vehicles also fared well, continuing the prior year’s trend toward greater fundraising concentration.

The numerator effect persisted

Despite a marked recovery in the denominator—the 1,000 largest US retirement funds grew 7 percent in the year ending September 2023, after falling 14 percent the prior year, for example 1 “U.S. retirement plans recover half of 2022 losses amid no-show recession,” Pensions and Investments , February 12, 2024. —many LPs remain overexposed to private markets relative to their target allocations. LPs started 2023 overweight: according to analysis from CEM Benchmarking, average allocations across PE, infrastructure, and real estate were at or above target allocations as of the beginning of the year. And the numerator grew throughout the year, as a lack of exits and rebounding valuations drove net asset values (NAVs) higher. While not all LPs strictly follow asset allocation targets, our analysis in partnership with global private markets firm StepStone Group suggests that an overallocation of just one percentage point can reduce planned commitments by as much as 10 to 12 percent per year for five years or more.

Despite these headwinds, recent surveys indicate that LPs remain broadly committed to private markets. In fact, the majority plan to maintain or increase allocations over the medium to long term.

Investors fled to known names and larger funds

Fundraising concentration reached its highest level in over a decade, as investors continued to shift new commitments in favor of the largest fund managers. The 25 most successful fundraisers collected 41 percent of aggregate commitments to closed-end funds (with the top five managers accounting for nearly half that total). Closed-end fundraising totals may understate the extent of concentration in the industry overall, as the largest managers also tend to be more successful in raising non-institutional capital.

While the largest funds grew even larger—the largest vehicles on record were raised in buyout, real estate, infrastructure, and private debt in 2023—smaller and newer funds struggled. Fewer than 1,700 funds of less than $1 billion were closed during the year, half as many as closed in 2022 and the fewest of any year since 2012. New manager formation also fell to the lowest level since 2012, with just 651 new firms launched in 2023.

Whether recent fundraising concentration and a spate of M&A activity signals the beginning of oft-rumored consolidation in the private markets remains uncertain, as a similar pattern developed in each of the last two fundraising downturns before giving way to renewed entrepreneurialism among general partners (GPs) and commitment diversification among LPs. Compared with how things played out in the last two downturns, perhaps this movie really is different, or perhaps we’re watching a trilogy reusing a familiar plotline.

Dry powder inventory spiked (again)

Private markets assets under management totaled $13.1 trillion as of June 30, 2023, and have grown nearly 20 percent per annum since 2018. Dry powder reserves—the amount of capital committed but not yet deployed—increased to $3.7 trillion, marking the ninth consecutive year of growth. Dry powder inventory—the amount of capital available to GPs expressed as a multiple of annual deployment—increased for the second consecutive year in PE, as new commitments continued to outpace deal activity. Inventory sat at 1.6 years in 2023, up markedly from the 0.9 years recorded at the end of 2021 but still within the historical range. NAV grew as well, largely driven by the reluctance of managers to exit positions and crystallize returns in a depressed multiple environment.

Private equity strategies diverged

Buyout and venture capital, the two largest PE sub-asset classes, charted wildly different courses over the past 18 months. Buyout notched its highest fundraising year ever in 2023, and its performance improved, with funds posting a (still paltry) 5 percent net internal rate of return through September 30. And although buyout deal volumes declined by 19 percent, 2023 was still the third-most-active year on record. In contrast, venture capital (VC) fundraising declined by nearly 60 percent, equaling its lowest total since 2015, and deal volume fell by 36 percent to the lowest level since 2019. VC funds returned –3 percent through September, posting negative returns for seven consecutive quarters. VC was the fastest-growing—as well as the highest-performing—PE strategy by a significant margin from 2010 to 2022, but investors appear to be reevaluating their approach in the current environment.

Private equity entry multiples contracted

PE buyout entry multiples declined by roughly one turn from 11.9 to 11.0 times EBITDA, slightly outpacing the decline in public market multiples (down from 12.1 to 11.3 times EBITDA), through the first nine months of 2023. For nearly a decade leading up to 2022, managers consistently sold assets into a higher-multiple environment than that in which they had bought those assets, providing a substantial performance tailwind for the industry. Nowhere has this been truer than in technology. After experiencing more than eight turns of multiple expansion from 2009 to 2021 (the most of any sector), technology multiples have declined by nearly three turns in the past two years, 50 percent more than in any other sector. Overall, roughly two-thirds of the total return for buyout deals that were entered in 2010 or later and exited in 2021 or before can be attributed to market multiple expansion and leverage. Now, with falling multiples and higher financing costs, revenue growth and margin expansion are taking center stage for GPs.

Real estate receded

Demand uncertainty, slowing rent growth, and elevated financing costs drove cap rates higher and made price discovery challenging, all of which weighed on deal volume, fundraising, and investment performance. Global closed-end fundraising declined 34 percent year over year, and funds returned −4 percent in the first nine months of the year, losing money for the first time since the 2007–08 global financial crisis. Capital shifted away from core and core-plus strategies as investors sought liquidity via redemptions in open-end vehicles, from which net outflows reached their highest level in at least two decades. Opportunistic strategies benefited from this shift, with investors focusing on capital appreciation over income generation in a market where alternative sources of yield have grown more attractive. Rising interest rates widened bid–ask spreads and impaired deal volume across food groups, including in what were formerly hot sectors: multifamily and industrial.

Private debt pays dividends

Debt again proved to be the most resilient private asset class against a turbulent market backdrop. Fundraising declined just 13 percent, largely driven by lower commitments to direct lending strategies, for which a slower PE deal environment has made capital deployment challenging. The asset class also posted the highest returns among all private asset classes through September 30. Many private debt securities are tied to floating rates, which enhance returns in a rising-rate environment. Thus far, managers appear to have successfully navigated the rising incidence of default and distress exhibited across the broader leveraged-lending market. Although direct lending deal volume declined from 2022, private lenders financed an all-time high 59 percent of leveraged buyout transactions last year and are now expanding into additional strategies to drive the next era of growth.

Infrastructure took a detour

After several years of robust growth and strong performance, infrastructure and natural resources fundraising declined by 53 percent to the lowest total since 2013. Supply-side timing is partially to blame: five of the seven largest infrastructure managers closed a flagship vehicle in 2021 or 2022, and none of those five held a final close last year. As in real estate, investors shied away from core and core-plus investments in a higher-yield environment. Yet there are reasons to believe infrastructure’s growth will bounce back. Limited partners (LPs) surveyed by McKinsey remain bullish on their deployment to the asset class, and at least a dozen vehicles targeting more than $10 billion were actively fundraising as of the end of 2023. Multiple recent acquisitions of large infrastructure GPs by global multi-asset-class managers also indicate marketwide conviction in the asset class’s potential.

Private markets still have work to do on diversity

Private markets firms are slowly improving their representation of females (up two percentage points over the prior year) and ethnic and racial minorities (up one percentage point). On some diversity metrics, including entry-level representation of women, private markets now compare favorably with corporate America. Yet broad-based parity remains elusive and too slow in the making. Ethnic, racial, and gender imbalances are particularly stark across more influential investing roles and senior positions. In fact, McKinsey’s research  reveals that at the current pace, it would take several decades for private markets firms to reach gender parity at senior levels. Increasing representation across all levels will require managers to take fresh approaches to hiring, retention, and promotion.

Artificial intelligence generating excitement

The transformative potential of generative AI was perhaps 2023’s hottest topic (beyond Taylor Swift). Private markets players are excited about the potential for the technology to optimize their approach to thesis generation, deal sourcing, investment due diligence, and portfolio performance, among other areas. While the technology is still nascent and few GPs can boast scaled implementations, pilot programs are already in flight across the industry, particularly within portfolio companies. Adoption seems nearly certain to accelerate throughout 2024.

Private markets in a slower era

If private markets investors entered 2023 hoping for a return to the heady days of 2021, they likely left the year disappointed. Many of the headwinds that emerged in the latter half of 2022 persisted throughout the year, pressuring fundraising, dealmaking, and performance. Inflation moderated somewhat over the course of the year but remained stubbornly elevated by recent historical standards. Interest rates started high and rose higher, increasing the cost of financing. A reinvigorated public equity market recovered most of 2022’s losses but did little to resolve the valuation uncertainty private market investors have faced for the past 18 months.

Within private markets, the denominator effect remained in play, despite the public market recovery, as the numerator continued to expand. An activity-dampening cycle emerged: higher cost of capital and lower multiples limited the ability or willingness of general partners (GPs) to exit positions; fewer exits, coupled with continuing capital calls, pushed LP allocations higher, thereby limiting their ability or willingness to make new commitments. These conditions weighed on managers’ ability to fundraise. Based on data reported as of year-end 2023, private markets fundraising fell 22 percent from the prior year to just over $1 trillion, the largest such drop since 2009 (Exhibit 1).

The impact of the fundraising environment was not felt equally among GPs. Continuing a trend that emerged in 2022, and consistent with prior downturns in fundraising, LPs favored larger vehicles and the scaled GPs that typically manage them. Smaller and newer managers struggled, and the number of sub–$1 billion vehicles and new firm launches each declined to its lowest level in more than a decade.

Despite the decline in fundraising, private markets assets under management (AUM) continued to grow, increasing 12 percent to $13.1 trillion as of June 30, 2023. 2023 fundraising was still the sixth-highest annual haul on record, pushing dry powder higher, while the slowdown in deal making limited distributions.

Investment performance across private market asset classes fell short of historical averages. Private equity (PE) got back in the black but generated the lowest annual performance in the past 15 years, excluding 2022. Closed-end real estate produced negative returns for the first time since 2009, as capitalization (cap) rates expanded across sectors and rent growth dissipated in formerly hot sectors, including multifamily and industrial. The performance of infrastructure funds was less than half of its long-term average and even further below the double-digit returns generated in 2021 and 2022. Private debt was the standout performer (if there was one), outperforming all other private asset classes and illustrating the asset class’s countercyclical appeal.

Private equity down but not out

Higher financing costs, lower multiples, and an uncertain macroeconomic environment created a challenging backdrop for private equity managers in 2023. Fundraising declined for the second year in a row, falling 15 percent to $649 billion, as LPs grappled with the denominator effect and a slowdown in distributions. Managers were on the fundraising trail longer to raise this capital: funds that closed in 2023 were open for a record-high average of 20.1 months, notably longer than 18.7 months in 2022 and 14.1 months in 2018. VC and growth equity strategies led the decline, dropping to their lowest level of cumulative capital raised since 2015. Fundraising in Asia fell for the fourth year of the last five, with the greatest decline in China.

Despite the difficult fundraising context, a subset of strategies and managers prevailed. Buyout managers collectively had their best fundraising year on record, raising more than $400 billion. Fundraising in Europe surged by more than 50 percent, resulting in the region’s biggest haul ever. The largest managers raised an outsized share of the total for a second consecutive year, making 2023 the most concentrated fundraising year of the last decade (Exhibit 2).

Despite the drop in aggregate fundraising, PE assets under management increased 8 percent to $8.2 trillion. Only a small part of this growth was performance driven: PE funds produced a net IRR of just 2.5 percent through September 30, 2023. Buyouts and growth equity generated positive returns, while VC lost money. PE performance, dating back to the beginning of 2022, remains negative, highlighting the difficulty of generating attractive investment returns in a higher interest rate and lower multiple environment. As PE managers devise value creation strategies to improve performance, their focus includes ensuring operating efficiency and profitability of their portfolio companies.

Deal activity volume and count fell sharply, by 21 percent and 24 percent, respectively, which continued the slower pace set in the second half of 2022. Sponsors largely opted to hold assets longer rather than lock in underwhelming returns. While higher financing costs and valuation mismatches weighed on overall deal activity, certain types of M&A gained share. Add-on deals, for example, accounted for a record 46 percent of total buyout deal volume last year.

Real estate recedes

For real estate, 2023 was a year of transition, characterized by a litany of new and familiar challenges. Pandemic-driven demand issues continued, while elevated financing costs, expanding cap rates, and valuation uncertainty weighed on commercial real estate deal volumes, fundraising, and investment performance.

Managers faced one of the toughest fundraising environments in many years. Global closed-end fundraising declined 34 percent to $125 billion. While fundraising challenges were widespread, they were not ubiquitous across strategies. Dollars continued to shift to large, multi-asset class platforms, with the top five managers accounting for 37 percent of aggregate closed-end real estate fundraising. In April, the largest real estate fund ever raised closed on a record $30 billion.

Capital shifted away from core and core-plus strategies as investors sought liquidity through redemptions in open-end vehicles and reduced gross contributions to the lowest level since 2009. Opportunistic strategies benefited from this shift, as investors turned their attention toward capital appreciation over income generation in a market where alternative sources of yield have grown more attractive.

In the United States, for instance, open-end funds, as represented by the National Council of Real Estate Investment Fiduciaries Fund Index—Open-End Equity (NFI-OE), recorded $13 billion in net outflows in 2023, reversing the trend of positive net inflows throughout the 2010s. The negative flows mainly reflected $9 billion in core outflows, with core-plus funds accounting for the remaining outflows, which reversed a 20-year run of net inflows.

As a result, the NAV in US open-end funds fell roughly 16 percent year over year. Meanwhile, global assets under management in closed-end funds reached a new peak of $1.7 trillion as of June 2023, growing 14 percent between June 2022 and June 2023.

Real estate underperformed historical averages in 2023, as previously high-performing multifamily and industrial sectors joined office in producing negative returns caused by slowing demand growth and cap rate expansion. Closed-end funds generated a pooled net IRR of −3.5 percent in the first nine months of 2023, losing money for the first time since the global financial crisis. The lone bright spot among major sectors was hospitality, which—thanks to a rush of postpandemic travel—returned 10.3 percent in 2023. 2 Based on NCREIFs NPI index. Hotels represent 1 percent of total properties in the index. As a whole, the average pooled lifetime net IRRs for closed-end real estate funds from 2011–20 vintages remained around historical levels (9.8 percent).

Global deal volume declined 47 percent in 2023 to reach a ten-year low of $650 billion, driven by widening bid–ask spreads amid valuation uncertainty and higher costs of financing (Exhibit 3). 3 CBRE, Real Capital Analytics Deal flow in the office sector remained depressed, partly as a result of continued uncertainty in the demand for space in a hybrid working world.

During a turbulent year for private markets, private debt was a relative bright spot, topping private markets asset classes in terms of fundraising growth, AUM growth, and performance.

Fundraising for private debt declined just 13 percent year over year, nearly ten percentage points less than the private markets overall. Despite the decline in fundraising, AUM surged 27 percent to $1.7 trillion. And private debt posted the highest investment returns of any private asset class through the first three quarters of 2023.

Private debt’s risk/return characteristics are well suited to the current environment. With interest rates at their highest in more than a decade, current yields in the asset class have grown more attractive on both an absolute and relative basis, particularly if higher rates sustain and put downward pressure on equity returns (Exhibit 4). The built-in security derived from debt’s privileged position in the capital structure, moreover, appeals to investors that are wary of market volatility and valuation uncertainty.

Direct lending continued to be the largest strategy in 2023, with fundraising for the mostly-senior-debt strategy accounting for almost half of the asset class’s total haul (despite declining from the previous year). Separately, mezzanine debt fundraising hit a new high, thanks to the closings of three of the largest funds ever raised in the strategy.

Over the longer term, growth in private debt has largely been driven by institutional investors rotating out of traditional fixed income in favor of private alternatives. Despite this growth in commitments, LPs remain underweight in this asset class relative to their targets. In fact, the allocation gap has only grown wider in recent years, a sharp contrast to other private asset classes, for which LPs’ current allocations exceed their targets on average. According to data from CEM Benchmarking, the private debt allocation gap now stands at 1.4 percent, which means that, in aggregate, investors must commit hundreds of billions in net new capital to the asset class just to reach current targets.

Private debt was not completely immune to the macroeconomic conditions last year, however. Fundraising declined for the second consecutive year and now sits 23 percent below 2021’s peak. Furthermore, though private lenders took share in 2023 from other capital sources, overall deal volumes also declined for the second year in a row. The drop was largely driven by a less active PE deal environment: private debt is predominantly used to finance PE-backed companies, though managers are increasingly diversifying their origination capabilities to include a broad new range of companies and asset types.

Infrastructure and natural resources take a detour

For infrastructure and natural resources fundraising, 2023 was an exceptionally challenging year. Aggregate capital raised declined 53 percent year over year to $82 billion, the lowest annual total since 2013. The size of the drop is particularly surprising in light of infrastructure’s recent momentum. The asset class had set fundraising records in four of the previous five years, and infrastructure is often considered an attractive investment in uncertain markets.

While there is little doubt that the broader fundraising headwinds discussed elsewhere in this report affected infrastructure and natural resources fundraising last year, dynamics specific to the asset class were at play as well. One issue was supply-side timing: nine of the ten largest infrastructure GPs did not close a flagship fund in 2023. Second was the migration of investor dollars away from core and core-plus investments, which have historically accounted for the bulk of infrastructure fundraising, in a higher rate environment.

The asset class had some notable bright spots last year. Fundraising for higher-returning opportunistic strategies more than doubled the prior year’s total (Exhibit 5). AUM grew 18 percent, reaching a new high of $1.5 trillion. Infrastructure funds returned a net IRR of 3.4 percent in 2023; this was below historical averages but still the second-best return among private asset classes. And as was the case in other asset classes, investors concentrated commitments in larger funds and managers in 2023, including in the largest infrastructure fund ever raised.

The outlook for the asset class, moreover, remains positive. Funds targeting a record amount of capital were in the market at year-end, providing a robust foundation for fundraising in 2024 and 2025. A recent spate of infrastructure GP acquisitions signal multi-asset managers’ long-term conviction in the asset class, despite short-term headwinds. Global megatrends like decarbonization and digitization, as well as revolutions in energy and mobility, have spurred new infrastructure investment opportunities around the world, particularly for value-oriented investors that are willing to take on more risk.

Private markets make measured progress in DEI

Diversity, equity, and inclusion (DEI) has become an important part of the fundraising, talent, and investing landscape for private market participants. Encouragingly, incremental progress has been made in recent years, including more diverse talent being brought to entry-level positions, investing roles, and investment committees. The scope of DEI metrics provided to institutional investors during fundraising has also increased in recent years: more than half of PE firms now provide data across investing teams, portfolio company boards, and portfolio company management (versus investment team data only). 4 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023.

In 2023, McKinsey surveyed 66 global private markets firms that collectively employ more than 60,000 people for the second annual State of diversity in global private markets report. 5 “ The state of diversity in global private markets: 2023 ,” McKinsey, August 22, 2023. The research offers insight into the representation of women and ethnic and racial minorities in private investing as of year-end 2022. In this chapter, we discuss where the numbers stand and how firms can bring a more diverse set of perspectives to the table.

The statistics indicate signs of modest advancement. Overall representation of women in private markets increased two percentage points to 35 percent, and ethnic and racial minorities increased one percentage point to 30 percent (Exhibit 6). Entry-level positions have nearly reached gender parity, with female representation at 48 percent. The share of women holding C-suite roles globally increased 3 percentage points, while the share of people from ethnic and racial minorities in investment committees increased 9 percentage points. There is growing evidence that external hiring is gradually helping close the diversity gap, especially at senior levels. For example, 33 percent of external hires at the managing director level were ethnic or racial minorities, higher than their existing representation level (19 percent).

Yet, the scope of the challenge remains substantial. Women and minorities continue to be underrepresented in senior positions and investing roles. They also experience uneven rates of progress due to lower promotion and higher attrition rates, particularly at smaller firms. Firms are also navigating an increasingly polarized workplace today, with additional scrutiny and a growing number of lawsuits against corporate diversity and inclusion programs, particularly in the US, which threatens to impact the industry’s pace of progress.

Fredrik Dahlqvist is a senior partner in McKinsey’s Stockholm office; Alastair Green  is a senior partner in the Washington, DC, office, where Paul Maia and Alexandra Nee  are partners; David Quigley  is a senior partner in the New York office, where Connor Mangan is an associate partner and Aditya Sanghvi  is a senior partner; Rahel Schneider is an associate partner in the Bay Area office; John Spivey is a partner in the Charlotte office; and Brian Vickery  is a partner in the Boston office.

The authors wish to thank Jonathan Christy, Louis Dufau, Vaibhav Gujral, Graham Healy-Day, Laura Johnson, Ryan Luby, Tripp Norton, Alastair Rami, Henri Torbey, and Alex Wolkomir for their contributions

The authors would also like to thank CEM Benchmarking and the StepStone Group for their partnership in this year's report.

This article was edited by Arshiya Khullar, an editor in the Gurugram office.

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